import copy import logging import pathlib import rapidjson import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta import pandas as pd import pandas_ta as pta from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import merge_informative_pair from pandas import DataFrame, Series from functools import reduce, partial from freqtrade.persistence import Trade, LocalTrade from datetime import datetime, timedelta import time from typing import Optional import warnings log = logging.getLogger(__name__) #log.setLevel(logging.DEBUG) warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning) ############################################################################################################# ## NostalgiaForInfinityX2 by iterativ ## ## https://github.com/iterativv/NostalgiaForInfinity ## ## ## ## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ## ## ## ############################################################################################################# ## GENERAL RECOMMENDATIONS ## ## ## ## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ## ## A pairlist with 40 to 80 pairs. Volume pairlist works well. ## ## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ## ## Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc). ## ## Ensure that you don't override any variables in you config.json. Especially ## ## the timeframe (must be 5m). ## ## use_exit_signal must set to true (or not set at all). ## ## exit_profit_only must set to false (or not set at all). ## ## ignore_roi_if_entry_signal must set to true (or not set at all). ## ## ## ############################################################################################################# ## DONATIONS ## ## ## ## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ## ## ETH (ERC20): 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ## ## BEP20/BSC (USDT, ETH, BNB, ...): 0x86A0B21a20b39d16424B7c8003E4A7e12d78ABEe ## ## TRC20/TRON (USDT, TRON, ...): TTAa9MX6zMLXNgWMhg7tkNormVHWCoq8Xk ## ## ## ## REFERRAL LINKS ## ## ## ## Binance: https://accounts.binance.com/en/register?ref=C68K26A9 (20% discount on trading fees) ## ## Kucoin: https://www.kucoin.com/r/af/QBSSS5J2 (20% lifetime discount on trading fees) ## ## Gate.io: https://www.gate.io/signup/8054544 (20% discount on trading fees) ## ## OKX: https://www.okx.com/join/11749725931 (20% discount on trading fees) ## ## ByBit: https://partner.bybit.com/b/nfi ## ## Huobi: https://www.huobi.com/en-us/v/register/double-invite/?inviter_id=11345710&invite_code=ubpt2223 ## ## (20% discount on trading fees) ## ## Bitvavo: https://account.bitvavo.com/create?a=D22103A4BC (no fees for the first € 1000) ## ############################################################################################################# class NostalgiaForInfinityX2(IStrategy): INTERFACE_VERSION = 3 def version(self) -> str: return "v12.0.80" # ROI table: minimal_roi = { "0": 100.0, } stoploss = -0.99 # Trailing stoploss (not used) trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.03 use_custom_stoploss = False # Optimal timeframe for the strategy. timeframe = '5m' info_timeframes = ['15m','1h','4h','1d'] # BTC informatives btc_info_timeframes = ['5m','15m','1h','4h','1d'] # Backtest Age Filter emulation has_bt_agefilter = False bt_min_age_days = 3 # Exchange Downtime protection has_downtime_protection = False # Do you want to use the hold feature? (with hold-trades.json) hold_support_enabled = True # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the "ask_strategy" section in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 480 # Normal mode bull tags normal_mode_bull_tags = ['force_entry', '1', '2', '3', '4', '5', '6'] # Normal mode bear tags normal_mode_bear_tags = ['11', '12', '13', '14', '15', '16'] # Pump mode bull tags pump_mode_bull_tags = ['21', '22'] # Pump mode bear tags pump_mode_bear_tags = ['31', '32'] # Quick mode bull tags quick_mode_bull_tags = ['41', '42', '43', '44'] # Quick mode bear tags quick_mode_bear_tags = ['51', '52', '53', '54'] # Rebuy mode bull tags rebuy_mode_bull_tags = ['61'] # Rebuy mode bear tags rebuy_mode_bear_tags = ['71'] # Long mode bull tags long_mode_bull_tags = ['81', '82'] # Long mode bear tags long_mode_bear_tags = ['91', '92'] # Stop thesholds. 0: Doom Bull, 1: Doom Bear, 2: u_e Bull, 3: u_e Bear, 4: u_e mins Bull, 5: u_e mins Bear. # 6: u_e ema % Bull, 7: u_e ema % Bear, 8: u_e RSI diff Bull, 9: u_e RSI diff Bear. # 10: enable Doom Bull, 11: enable Doom Bear, 12: enable u_e Bull, 13: enable u_e Bear. stop_thresholds_normal = [-0.2, -0.2, -0.025, -0.025, 720, 720, 0.016, 0.016, 24.0, 24.0, True, True, True, True] stop_thresholds_pump = [-0.2, -0.2, -0.025, -0.025, 720, 720, 0.016, 0.016, 24.0, 24.0, True, True, True, True] stop_thresholds_quick = [-0.2, -0.2, -0.025, -0.025, 720, 720, 0.016, 0.016, 24.0, 24.0, True, True, True, True] stop_thresholds_rebuy = [-0.2, -0.2, -0.025, -0.025, 720, 720, 0.016, 0.016, 24.0, 24.0, True, True, True, True] stop_thresholds_long = [-0.2, -0.2, -0.025, -0.025, 720, 720, 0.016, 0.016, 24.0, 24.0, True, True, True, True] # Rebuy mode minimum number of free slots rebuy_mode_min_free_slots = 2 # Position adjust feature position_adjustment_enable = True stake_rebuy_mode_bull_multiplier = 0.33 pa_rebuy_mode_bull_max = 2 pa_rebuy_mode_bull_pcts = (-0.02, -0.04, -0.04) pa_rebuy_mode_bull_multi = (1.0, 1.0, 1.0) stake_rebuy_mode_bear_multiplier = 0.33 pa_rebuy_mode_bear_max = 2 pa_rebuy_mode_bear_pcts = (-0.02, -0.04, -0.04) pa_rebuy_mode_bear_multi = (1.0, 1.0, 1.0) ############################################################# # Buy side configuration buy_params = { # Enable/Disable conditions # ------------------------------------------------------- "buy_condition_1_enable": True, "buy_condition_2_enable": True, "buy_condition_3_enable": True, "buy_condition_4_enable": True, "buy_condition_5_enable": True, "buy_condition_6_enable": True, "buy_condition_11_enable": True, "buy_condition_12_enable": True, "buy_condition_13_enable": True, "buy_condition_14_enable": True, "buy_condition_15_enable": True, "buy_condition_16_enable": True, "buy_condition_21_enable": True, "buy_condition_22_enable": True, "buy_condition_31_enable": True, "buy_condition_32_enable": True, "buy_condition_41_enable": True, "buy_condition_42_enable": True, "buy_condition_43_enable": True, "buy_condition_44_enable": True, "buy_condition_51_enable": True, "buy_condition_52_enable": True, "buy_condition_53_enable": True, "buy_condition_54_enable": True, "buy_condition_61_enable": True, "buy_condition_71_enable": True, "buy_condition_81_enable": True, "buy_condition_82_enable": True, "buy_condition_91_enable": True, "buy_condition_92_enable": True, } buy_protection_params = {} ############################################################# # CACHES hold_trades_cache = None target_profit_cache = None ############################################################# def __init__(self, config: dict) -> None: super().__init__(config) if (('exit_profit_only' in self.config and self.config['exit_profit_only']) or ('sell_profit_only' in self.config and self.config['sell_profit_only'])): self.exit_profit_only = True if ('stop_thresholds_normal' in self.config): self.stop_thresholds_normal = self.config['stop_thresholds_normal'] if ('stop_thresholds_pump' in self.config): self.stop_thresholds_pump = self.config['stop_thresholds_pump'] if ('stop_thresholds_quick' in self.config): self.stop_thresholds_quick = self.config['stop_thresholds_quick'] if ('stop_thresholds_rebuy' in self.config): self.stop_thresholds_rebuy = self.config['stop_thresholds_rebuy'] if ('stop_thresholds_long' in self.config): self.stop_thresholds_long = self.config['stop_thresholds_long'] if self.target_profit_cache is None: bot_name = "" if ('bot_name' in self.config): bot_name = self.config["bot_name"] + "-" self.target_profit_cache = Cache( self.config["user_data_dir"] / ("nfix2-profit_max-" + bot_name + self.config["exchange"]["name"] + "-" + self.config["stake_currency"] + ("-(backtest)" if (self.config['runmode'].value == 'backtest') else "") + ".json") ) # If the cached data hasn't changed, it's a no-op self.target_profit_cache.save() def get_ticker_indicator(self): return int(self.timeframe[:-1]) def exit_normal_bull(self, pair: str, current_rate: float, current_profit: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', enter_tags) -> tuple: sell = False # Original sell signals sell, signal_name = self.exit_normal_bull_signals(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Main sell signals if not sell: sell, signal_name = self.exit_normal_bull_main(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Williams %R based sells if not sell: sell, signal_name = self.exit_normal_bull_r(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Stoplosses if not sell: sell, signal_name = self.exit_normal_bull_stoploss(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Profit Target Signal # Check if pair exist on target_profit_cache if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_rate = self.target_profit_cache.data[pair]['rate'] previous_profit = self.target_profit_cache.data[pair]['profit'] previous_sell_reason = self.target_profit_cache.data[pair]['sell_reason'] previous_time_profit_reached = datetime.fromisoformat(self.target_profit_cache.data[pair]['time_profit_reached']) sell_max, signal_name_max = self.normal_bull_exit_profit_target(pair, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) if sell_max and signal_name_max is not None: return True, f"{signal_name_max}_m" if (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_normal_bull_stoploss_doom"]): # Update the target, raise it. mark_pair, mark_signal = self.normal_bull_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) # Add the pair to the list, if a sell triggered and conditions met if sell and signal_name is not None: previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if ( (previous_profit is None) or (previous_profit < current_profit) ): mark_pair, mark_signal = self.normal_bull_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" else: if ( (current_profit >= 0.01) ): previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (previous_profit is None) or (previous_profit < current_profit): mark_signal = "exit_profit_normal_bull_max" self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) if (signal_name not in ["exit_profit_normal_bull_max", "exit_normal_bull_stoploss_doom", "exit_normal_bull_stoploss_u_e"]): if sell and (signal_name is not None): return True, f"{signal_name}" return False, None def normal_bull_mark_profit_target(self, pair: str, sell: bool, signal_name: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1) -> tuple: if sell and (signal_name is not None): return pair, signal_name return None, None def normal_bull_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) -> tuple: if (previous_sell_reason in ["exit_normal_bull_stoploss_doom"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < -0.18): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.1): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.04): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason else: if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_normal_bull_stoploss_u_e"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_profit_normal_bull_max"]): if (current_profit < -0.08): # profit is under the threshold, cancel it self._remove_profit_target(pair) return False, None if (0.001 <= current_profit < 0.01): if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (0.01 <= current_profit < 0.02): if (current_profit < (previous_profit - 0.02)): return True, previous_sell_reason elif (0.02 <= current_profit < 0.03): if (current_profit < (previous_profit - 0.03)): return True, previous_sell_reason elif (0.03 <= current_profit < 0.05): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (0.05 <= current_profit < 0.08): if (current_profit < (previous_profit - 0.05)): return True, previous_sell_reason elif (0.08 <= current_profit < 0.12): if (current_profit < (previous_profit - 0.06)): return True, previous_sell_reason elif (0.12 <= current_profit): if (current_profit < (previous_profit - 0.07)): return True, previous_sell_reason else: return False, None return False, None def exit_normal_bull_signals(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: # Sell signal 1 if (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']) and (previous_candle_3['close'] > previous_candle_3['bb20_2_upp']) and (previous_candle_4['close'] > previous_candle_4['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_normal_bull_1_1_1' else: if (current_profit > 0.01): return True, 'exit_normal_bull_1_2_1' # Sell signal 2 elif (last_candle['rsi_14'] > 80.0) and (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_normal_bull_2_1_1' else: if (current_profit > 0.01): return True, 'exit_normal_bull_2_2_1' # Sell signal 3 elif (last_candle['rsi_14'] > 85.0) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_normal_bull_3_1_1' else: if (current_profit > 0.01): return True, 'exit_normal_bull_3_2_1' # Sell signal 4 elif (last_candle['rsi_14'] > 80.0) and (last_candle['rsi_14_1h'] > 80.0) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_normal_bull_4_1_1' else: if (current_profit > 0.01): return True, 'exit_normal_bull_4_2_1' # Sell signal 6 elif (last_candle['close'] < last_candle['ema_200']) and (last_candle['close'] > last_candle['ema_50']) and (last_candle['rsi_14'] > 79.0): if (current_profit > 0.01): return True, 'exit_normal_bull_6_1' # Sell signal 7 elif (last_candle['rsi_14_1h'] > 79.0) and (last_candle['crossed_below_ema_12_26']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_normal_bull_7_1_1' else: if (current_profit > 0.01): return True, 'exit_normal_bull_7_2_1' # Sell signal 8 elif (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp_1h'] * 1.08): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_normal_bull_8_1_1' else: if (current_profit > 0.01): return True, 'exit_normal_bull_8_2_1' return False, None def exit_normal_bull_main(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if (last_candle['close'] > last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 20.0): return True, 'exit_normal_bull_o_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 28.0): return True, 'exit_normal_bull_o_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 30.0): return True, 'exit_normal_bull_o_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 32.0): return True, 'exit_normal_bull_o_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 34.0): return True, 'exit_normal_bull_o_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 36.0): return True, 'exit_normal_bull_o_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 38.0): return True, 'exit_normal_bull_o_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 40.0): return True, 'exit_normal_bull_o_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 42.0): return True, 'exit_normal_bull_o_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 44.0): return True, 'exit_normal_bull_o_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 46.0): return True, 'exit_normal_bull_o_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 44.0): return True, 'exit_normal_bull_o_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 42.0): return True, 'exit_normal_bull_o_12' elif (last_candle['close'] < last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 22.0): return True, 'exit_normal_bull_u_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 30.0): return True, 'exit_normal_bull_u_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 32.0): return True, 'exit_normal_bull_u_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 34.0): return True, 'exit_normal_bull_u_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 36.0): return True, 'exit_normal_bull_u_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 38.0): return True, 'exit_normal_bull_u_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 40.0): return True, 'exit_normal_bull_u_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 42.0): return True, 'exit_normal_bull_u_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 44.0): return True, 'exit_normal_bull_u_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 46.0): return True, 'exit_normal_bull_u_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 48.0): return True, 'exit_normal_bull_u_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 46.0): return True, 'exit_normal_bull_u_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 44.0): return True, 'exit_normal_bull_u_12' return False, None def exit_normal_bull_r(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if 0.01 > current_profit >= 0.001: if (last_candle['r_480'] > -0.1): return True, 'exit_normal_bull_w_0_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bull_w_0_2' elif 0.02 > current_profit >= 0.01: if (last_candle['r_480'] > -0.2): return True, 'exit_normal_bull_w_1_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bull_w_1_2' elif 0.03 > current_profit >= 0.02: if (last_candle['r_480'] > -0.3): return True, 'exit_normal_bull_w_2_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bull_w_2_2' elif 0.04 > current_profit >= 0.03: if (last_candle['r_480'] > -0.4): return True, 'exit_normal_bull_w_3_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bull_w_3_2' elif 0.05 > current_profit >= 0.04: if (last_candle['r_480'] > -0.5): return True, 'exit_normal_bull_w_4_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bull_w_4_2' elif 0.06 > current_profit >= 0.05: if (last_candle['r_480'] > -0.6): return True, 'exit_normal_bull_w_5_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bull_w_5_2' elif 0.07 > current_profit >= 0.06: if (last_candle['r_480'] > -0.7): return True, 'exit_normal_bull_w_6_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bull_w_6_2' elif 0.08 > current_profit >= 0.07: if (last_candle['r_480'] > -0.8): return True, 'exit_normal_bull_w_7_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bull_w_7_2' elif 0.09 > current_profit >= 0.08: if (last_candle['r_480'] > -0.9): return True, 'exit_normal_bull_w_8_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bull_w_8_2' elif 0.1 > current_profit >= 0.09: if (last_candle['r_480'] > -1.0): return True, 'exit_normal_bull_w_9_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bull_w_9_2' elif 0.12 > current_profit >= 0.1: if (last_candle['r_480'] > -1.1): return True, 'exit_normal_bull_w_10_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bull_w_10_2' elif 0.2 > current_profit >= 0.12: if (last_candle['r_480'] > -0.4): return True, 'exit_normal_bull_w_11_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bull_w_11_2' elif current_profit >= 0.2: if (last_candle['r_480'] > -0.2): return True, 'exit_normal_bull_w_12_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0): return True, 'exit_normal_bull_w_12_2' return False, None def exit_normal_bull_stoploss(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: is_backtest = self.dp.runmode.value == 'backtest' # Stoploss doom if ( (self.stop_thresholds_normal[10]) and (current_profit < self.stop_thresholds_normal[0]) ): return True, 'exit_normal_bull_stoploss_doom' # Under & near EMA200, local uptrend move if ( (self.stop_thresholds_normal[12]) and (current_profit < self.stop_thresholds_normal[2]) and (last_candle['close'] < last_candle['ema_200']) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_normal[6]) and (last_candle['rsi_14'] > previous_candle_1['rsi_14']) and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_normal[8])) and (current_time - timedelta(minutes=self.stop_thresholds_normal[4]) > trade.open_date_utc) # temporary and (trade.open_date_utc.replace(tzinfo=None) >= datetime(2022, 12, 25) or is_backtest) ): return True, 'exit_normal_bull_stoploss_u_e' return False, None def exit_normal_bear(self, pair: str, current_rate: float, current_profit: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', enter_tags) -> tuple: sell = False # Original sell signals sell, signal_name = self.exit_normal_bear_signals(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Main sell signals if not sell: sell, signal_name = self.exit_normal_bear_main(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Williams %R based sells if not sell: sell, signal_name = self.exit_normal_bear_r(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Stoplosses if not sell: sell, signal_name = self.exit_normal_bear_stoploss(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Profit Target Signal # Check if pair exist on target_profit_cache if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_rate = self.target_profit_cache.data[pair]['rate'] previous_profit = self.target_profit_cache.data[pair]['profit'] previous_sell_reason = self.target_profit_cache.data[pair]['sell_reason'] previous_time_profit_reached = datetime.fromisoformat(self.target_profit_cache.data[pair]['time_profit_reached']) sell_max, signal_name_max = self.normal_bear_exit_profit_target(pair, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) if sell_max and signal_name_max is not None: return True, f"{signal_name_max}_m" if (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_normal_bear_stoploss_doom"]): # Update the target, raise it. mark_pair, mark_signal = self.normal_bear_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) # Add the pair to the list, if a sell triggered and conditions met if sell and signal_name is not None: previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if ( (previous_profit is None) or (previous_profit < current_profit) ): mark_pair, mark_signal = self.normal_bear_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" else: if ( (current_profit >= 0.01) ): previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (previous_profit is None) or (previous_profit < current_profit): mark_signal = "exit_profit_normal_bear_max" self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) if (signal_name not in ["exit_profit_normal_bear_max", "exit_normal_bear_stoploss_doom", "exit_normal_bear_stoploss_u_e"]): if sell and (signal_name is not None): return True, f"{signal_name}" return False, None def normal_bear_mark_profit_target(self, pair: str, sell: bool, signal_name: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1) -> tuple: if sell and (signal_name is not None): return pair, signal_name return None, None def normal_bear_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) -> tuple: if (previous_sell_reason in ["exit_normal_bear_stoploss_doom"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < -0.18): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.1): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.04): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason else: if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_normal_bear_stoploss_u_e"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_profit_normal_bear_max"]): if (current_profit < -0.08): # profit is under the threshold, cancel it self._remove_profit_target(pair) return False, None if (0.001 <= current_profit < 0.01): if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (0.01 <= current_profit < 0.02): if (current_profit < (previous_profit - 0.02)): return True, previous_sell_reason elif (0.02 <= current_profit < 0.03): if (current_profit < (previous_profit - 0.03)): return True, previous_sell_reason elif (0.03 <= current_profit < 0.05): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (0.05 <= current_profit < 0.08): if (current_profit < (previous_profit - 0.05)): return True, previous_sell_reason elif (0.08 <= current_profit < 0.12): if (current_profit < (previous_profit - 0.06)): return True, previous_sell_reason elif (0.12 <= current_profit): if (current_profit < (previous_profit - 0.07)): return True, previous_sell_reason else: return False, None return False, None def exit_normal_bear_signals(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: # Sell signal 1 if (last_candle['rsi_14'] > 78.0) and (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']) and (previous_candle_3['close'] > previous_candle_3['bb20_2_upp']) and (previous_candle_4['close'] > previous_candle_4['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_normal_bear_1_1_1' else: if (current_profit > 0.01): return True, 'exit_normal_bear_1_2_1' # Sell signal 2 elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_normal_bear_2_1_1' else: if (current_profit > 0.01): return True, 'exit_normal_bear_2_2_1' # Sell signal 3 elif (last_candle['rsi_14'] > 84.0) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_normal_bear_3_1_1' else: if (current_profit > 0.01): return True, 'exit_normal_bear_3_2_1' # Sell signal 4 elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_1h'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_normal_bear_4_1_1' else: if (current_profit > 0.01): return True, 'exit_normal_bear_4_2_1' # Sell signal 6 elif (last_candle['close'] < last_candle['ema_200']) and (last_candle['close'] > last_candle['ema_50']) and (last_candle['rsi_14'] > 78.5): if (current_profit > 0.01): return True, 'exit_normal_bear_6_1' # Sell signal 7 elif (last_candle['rsi_14_1h'] > 79.0) and (last_candle['crossed_below_ema_12_26']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_normal_bear_7_1_1' else: if (current_profit > 0.01): return True, 'exit_normal_bear_7_2_1' # Sell signal 8 elif (last_candle['close'] > last_candle['bb20_2_upp_1h'] * 1.07) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_normal_bear_8_1_1' else: if (current_profit > 0.01): return True, 'exit_normal_bear_8_2_1' return False, None def exit_normal_bear_main(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if (last_candle['close'] > last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 22.0): return True, 'exit_normal_bear_o_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 30.0): return True, 'exit_normal_bear_o_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 32.0): return True, 'exit_normal_bear_o_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 34.0): return True, 'exit_normal_bear_o_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 36.0): return True, 'exit_normal_bear_o_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 38.0): return True, 'exit_normal_bear_o_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 40.0): return True, 'exit_normal_bear_o_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 42.0): return True, 'exit_normal_bear_o_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 44.0): return True, 'exit_normal_bear_o_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 46.0): return True, 'exit_normal_bear_o_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 48.0): return True, 'exit_normal_bear_o_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 46.0): return True, 'exit_normal_bear_o_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 44.0): return True, 'exit_normal_bear_o_12' elif (last_candle['close'] < last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 24.0): return True, 'exit_normal_bear_u_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 32.0): return True, 'exit_normal_bear_u_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 34.0): return True, 'exit_normal_bear_u_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 36.0): return True, 'exit_normal_bear_u_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 38.0): return True, 'exit_normal_bear_u_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 40.0): return True, 'exit_normal_bear_u_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 42.0): return True, 'exit_normal_bear_u_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 44.0): return True, 'exit_normal_bear_u_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 46.0): return True, 'exit_normal_bear_u_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 48.0): return True, 'exit_normal_bear_u_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 50.0): return True, 'exit_normal_bear_u_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 48.0): return True, 'exit_normal_bear_u_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 46.0): return True, 'exit_normal_bear_u_12' return False, None def exit_normal_bear_r(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if 0.01 > current_profit >= 0.001: if (last_candle['r_480'] > -0.1): return True, 'exit_normal_bear_w_0_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bear_w_0_2' elif 0.02 > current_profit >= 0.01: if (last_candle['r_480'] > -0.2): return True, 'exit_normal_bear_w_1_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bear_w_1_2' elif 0.03 > current_profit >= 0.02: if (last_candle['r_480'] > -0.3): return True, 'exit_normal_bear_w_2_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bear_w_2_2' elif 0.04 > current_profit >= 0.03: if (last_candle['r_480'] > -0.4): return True, 'exit_normal_bear_w_3_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bear_w_3_2' elif 0.05 > current_profit >= 0.04: if (last_candle['r_480'] > -0.5): return True, 'exit_normal_bear_w_4_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bear_w_4_2' elif 0.06 > current_profit >= 0.05: if (last_candle['r_480'] > -0.6): return True, 'exit_normal_bear_w_5_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bear_w_5_2' elif 0.07 > current_profit >= 0.06: if (last_candle['r_480'] > -0.7): return True, 'exit_normal_bear_w_6_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bear_w_6_2' elif 0.08 > current_profit >= 0.07: if (last_candle['r_480'] > -0.8): return True, 'exit_normal_bear_w_7_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bear_w_7_2' elif 0.09 > current_profit >= 0.08: if (last_candle['r_480'] > -0.9): return True, 'exit_normal_bear_w_8_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bear_w_8_2' elif 0.1 > current_profit >= 0.09: if (last_candle['r_480'] > -1.0): return True, 'exit_normal_bear_w_9_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bear_w_9_2' elif 0.12 > current_profit >= 0.1: if (last_candle['r_480'] > -1.1): return True, 'exit_normal_bear_w_10_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bear_w_10_2' elif 0.2 > current_profit >= 0.12: if (last_candle['r_480'] > -0.4): return True, 'exit_normal_bear_w_11_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_normal_bear_w_11_2' elif current_profit >= 0.2: if (last_candle['r_480'] > -0.2): return True, 'exit_normal_bear_w_12_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0): return True, 'exit_normal_bear_w_12_2' return False, None def exit_normal_bear_stoploss(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: is_backtest = self.dp.runmode.value == 'backtest' # Stoploss doom if ( (self.stop_thresholds_normal[11]) and (current_profit < self.stop_thresholds_normal[1]) ): return True, 'exit_normal_bear_stoploss_doom' # Under & near EMA200, local uptrend move if ( (self.stop_thresholds_normal[13]) and (current_profit < self.stop_thresholds_normal[3]) and (last_candle['close'] < last_candle['ema_200']) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_normal[7]) and (last_candle['rsi_14'] > previous_candle_1['rsi_14']) and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_normal[9])) and (current_time - timedelta(minutes=self.stop_thresholds_normal[5]) > trade.open_date_utc) # temporary and (trade.open_date_utc.replace(tzinfo=None) >= datetime(2022, 12, 25) or is_backtest) ): return True, 'exit_normal_bear_stoploss_u_e' return False, None def exit_pump_bull(self, pair: str, current_rate: float, current_profit: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', enter_tags) -> tuple: sell = False # Original sell signals sell, signal_name = self.exit_pump_bull_signals(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Main sell signals if not sell: sell, signal_name = self.exit_pump_bull_main(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Williams %R based sells if not sell: sell, signal_name = self.exit_pump_bull_r(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Stoplosses if not sell: sell, signal_name = self.exit_pump_bull_stoploss(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Profit Target Signal # Check if pair exist on target_profit_cache if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_rate = self.target_profit_cache.data[pair]['rate'] previous_profit = self.target_profit_cache.data[pair]['profit'] previous_sell_reason = self.target_profit_cache.data[pair]['sell_reason'] previous_time_profit_reached = datetime.fromisoformat(self.target_profit_cache.data[pair]['time_profit_reached']) sell_max, signal_name_max = self.pump_bull_exit_profit_target(pair, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) if sell_max and signal_name_max is not None: return True, f"{signal_name_max}_m" if (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_pump_bull_stoploss_doom"]): # Update the target, raise it. mark_pair, mark_signal = self.pump_bull_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) # Add the pair to the list, if a sell triggered and conditions met if sell and signal_name is not None: previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if ( (previous_profit is None) or (previous_profit < current_profit) ): mark_pair, mark_signal = self.pump_bull_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" else: if ( (current_profit >= 0.01) ): previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (previous_profit is None) or (previous_profit < current_profit): mark_signal = "exit_profit_pump_bull_max" self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) if (signal_name not in ["exit_profit_pump_bull_max", "exit_pump_bull_stoploss_doom"]): if sell and (signal_name is not None): return True, f"{signal_name}" return False, None def pump_bull_mark_profit_target(self, pair: str, sell: bool, signal_name: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1) -> tuple: if sell and (signal_name is not None): return pair, signal_name return None, None def pump_bull_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) -> tuple: if (previous_sell_reason in ["exit_pump_bull_stoploss_doom"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < -0.18): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.1): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.04): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason else: if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_pump_bull_stoploss_u_e"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_profit_pump_bull_max"]): if (current_profit < -0.08): # profit is under the threshold, cancel self._remove_profit_target(pair) return False, None if (0.001 <= current_profit < 0.01): if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (0.01 <= current_profit < 0.02): if (current_profit < (previous_profit - 0.02)): return True, previous_sell_reason elif (0.02 <= current_profit < 0.03): if (current_profit < (previous_profit - 0.03)): return True, previous_sell_reason elif (0.03 <= current_profit < 0.05): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (0.05 <= current_profit < 0.08): if (current_profit < (previous_profit - 0.05)): return True, previous_sell_reason elif (0.08 <= current_profit < 0.12): if (current_profit < (previous_profit - 0.06)): return True, previous_sell_reason elif (0.12 <= current_profit): if (current_profit < (previous_profit - 0.07)): return True, previous_sell_reason else: return False, None return False, None def exit_pump_bull_signals(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: # Sell signal 1 if (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']) and (previous_candle_3['close'] > previous_candle_3['bb20_2_upp']) and (previous_candle_4['close'] > previous_candle_4['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_pump_bull_1_1_1' else: if (current_profit > 0.01): return True, 'exit_pump_bull_1_2_1' # Sell signal 2 elif (last_candle['rsi_14'] > 80.0) and (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_pump_bull_2_1_1' else: if (current_profit > 0.01): return True, 'exit_pump_bull_2_2_1' # Sell signal 3 elif (last_candle['rsi_14'] > 85.0) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_pump_bull_3_1_1' else: if (current_profit > 0.01): return True, 'exit_pump_bull_3_2_1' # Sell signal 4 elif (last_candle['rsi_14'] > 80.0) and (last_candle['rsi_14_1h'] > 80.0) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_pump_bull_4_1_1' else: if (current_profit > 0.01): return True, 'exit_pump_bull_4_2_1' # Sell signal 6 elif (last_candle['close'] < last_candle['ema_200']) and (last_candle['close'] > last_candle['ema_50']) and (last_candle['rsi_14'] > 79.0): if (current_profit > 0.01): return True, 'exit_pump_bull_6_1' # Sell signal 7 elif (last_candle['rsi_14_1h'] > 79.0) and (last_candle['crossed_below_ema_12_26']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_pump_bull_7_1_1' else: if (current_profit > 0.01): return True, 'exit_pump_bull_7_2_1' # Sell signal 8 elif (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp_1h'] * 1.08): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_pump_bull_8_1_1' else: if (current_profit > 0.01): return True, 'exit_pump_bull_8_2_1' return False, None def exit_pump_bull_main(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if (last_candle['close'] > last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 20.0): return True, 'exit_pump_bull_o_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 28.0): return True, 'exit_pump_bull_o_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 30.0): return True, 'exit_pump_bull_o_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 32.0): return True, 'exit_pump_bull_o_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 34.0): return True, 'exit_pump_bull_o_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 36.0): return True, 'exit_pump_bull_o_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 38.0): return True, 'exit_pump_bull_o_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 40.0): return True, 'exit_pump_bull_o_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 42.0): return True, 'exit_pump_bull_o_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 44.0): return True, 'exit_pump_bull_o_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 46.0): return True, 'exit_pump_bull_o_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 44.0): return True, 'exit_pump_bull_o_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 42.0): return True, 'exit_pump_bull_o_12' elif (last_candle['close'] < last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 22.0): return True, 'exit_pump_bull_u_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 30.0): return True, 'exit_pump_bull_u_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 32.0): return True, 'exit_pump_bull_u_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 34.0): return True, 'exit_pump_bull_u_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 36.0): return True, 'exit_pump_bull_u_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 38.0): return True, 'exit_pump_bull_u_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 40.0): return True, 'exit_pump_bull_u_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 42.0): return True, 'exit_pump_bull_u_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 44.0): return True, 'exit_pump_bull_u_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 46.0): return True, 'exit_pump_bull_u_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 48.0): return True, 'exit_pump_bull_u_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 46.0): return True, 'exit_pump_bull_u_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 44.0): return True, 'exit_pump_bull_u_12' return False, None def exit_pump_bull_r(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if 0.01 > current_profit >= 0.001: if (last_candle['r_480'] > -0.1): return True, 'exit_pump_bull_w_0_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bull_w_0_2' elif 0.02 > current_profit >= 0.01: if (last_candle['r_480'] > -0.2): return True, 'exit_pump_bull_w_1_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bull_w_1_2' elif 0.03 > current_profit >= 0.02: if (last_candle['r_480'] > -0.3): return True, 'exit_pump_bull_w_2_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bull_w_2_2' elif 0.04 > current_profit >= 0.03: if (last_candle['r_480'] > -0.4): return True, 'exit_pump_bull_w_3_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bull_w_3_2' elif 0.05 > current_profit >= 0.04: if (last_candle['r_480'] > -0.5): return True, 'exit_pump_bull_w_4_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bull_w_4_2' elif 0.06 > current_profit >= 0.05: if (last_candle['r_480'] > -0.6): return True, 'exit_pump_bull_w_5_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bull_w_5_2' elif 0.07 > current_profit >= 0.06: if (last_candle['r_480'] > -0.7): return True, 'exit_pump_bull_w_6_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bull_w_6_2' elif 0.08 > current_profit >= 0.07: if (last_candle['r_480'] > -0.8): return True, 'exit_pump_bull_w_7_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bull_w_7_2' elif 0.09 > current_profit >= 0.08: if (last_candle['r_480'] > -0.9): return True, 'exit_pump_bull_w_8_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bull_w_8_2' elif 0.1 > current_profit >= 0.09: if (last_candle['r_480'] > -1.0): return True, 'exit_pump_bull_w_9_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bull_w_9_2' elif 0.12 > current_profit >= 0.1: if (last_candle['r_480'] > -1.1): return True, 'exit_pump_bull_w_10_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bull_w_10_2' elif 0.2 > current_profit >= 0.12: if (last_candle['r_480'] > -0.4): return True, 'exit_pump_bull_w_11_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bull_w_11_2' elif current_profit >= 0.2: if (last_candle['r_480'] > -0.2): return True, 'exit_pump_bull_w_12_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0): return True, 'exit_pump_bull_w_12_2' return False, None def exit_pump_bull_stoploss(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: is_backtest = self.dp.runmode.value == 'backtest' # Stoploss doom if ( (self.stop_thresholds_pump[10]) and (current_profit < self.stop_thresholds_pump[0]) ): return True, 'exit_pump_bull_stoploss_doom' # Under & near EMA200, local uptrend move if ( (self.stop_thresholds_pump[12]) and (current_profit < self.stop_thresholds_pump[2]) and (last_candle['close'] < last_candle['ema_200']) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_pump[6]) and (last_candle['rsi_14'] > previous_candle_1['rsi_14']) and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_pump[8])) and (current_time - timedelta(minutes=self.stop_thresholds_pump[4]) > trade.open_date_utc) # temporary and (trade.open_date_utc.replace(tzinfo=None) >= datetime(2022, 12, 25) or is_backtest) ): return True, 'exit_pump_bull_stoploss_u_e' return False, None def exit_pump_bear(self, pair: str, current_rate: float, current_profit: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', enter_tags) -> tuple: sell = False # Original sell signals sell, signal_name = self.exit_pump_bear_signals(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Main sell signals if not sell: sell, signal_name = self.exit_pump_bear_main(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Williams %R based sells if not sell: sell, signal_name = self.exit_pump_bear_r(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Stoplosses if not sell: sell, signal_name = self.exit_pump_bear_stoploss(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Profit Target Signal # Check if pair exist on target_profit_cache if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_rate = self.target_profit_cache.data[pair]['rate'] previous_profit = self.target_profit_cache.data[pair]['profit'] previous_sell_reason = self.target_profit_cache.data[pair]['sell_reason'] previous_time_profit_reached = datetime.fromisoformat(self.target_profit_cache.data[pair]['time_profit_reached']) sell_max, signal_name_max = self.pump_bear_exit_profit_target(pair, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) if sell_max and signal_name_max is not None: return True, f"{signal_name_max}_m" if (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_pump_bear_stoploss_doom"]): # Update the target, raise it. mark_pair, mark_signal = self.pump_bear_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) # Add the pair to the list, if a sell triggered and conditions met if sell and signal_name is not None: previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if ( (previous_profit is None) or (previous_profit < current_profit) ): mark_pair, mark_signal = self.pump_bear_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" else: if ( (current_profit >= 0.01) ): previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (previous_profit is None) or (previous_profit < current_profit): mark_signal = "exit_profit_pump_bear_max" self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) if (signal_name not in ["exit_profit_pump_bear_max", "exit_pump_bear_stoploss_doom"]): if sell and (signal_name is not None): return True, f"{signal_name}" return False, None def pump_bear_mark_profit_target(self, pair: str, sell: bool, signal_name: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1) -> tuple: if sell and (signal_name is not None): return pair, signal_name return None, None def pump_bear_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) -> tuple: if (previous_sell_reason in ["exit_pump_bear_stoploss_doom"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < -0.18): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.1): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.04): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason else: if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_pump_bear_stoploss_u_e"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_profit_pump_bear_max"]): if (current_profit < -0.08): # profit is under the threshold, cancel self._remove_profit_target(pair) return False, None if (0.001 <= current_profit < 0.01): if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (0.01 <= current_profit < 0.02): if (current_profit < (previous_profit - 0.02)): return True, previous_sell_reason elif (0.02 <= current_profit < 0.03): if (current_profit < (previous_profit - 0.03)): return True, previous_sell_reason elif (0.03 <= current_profit < 0.05): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (0.05 <= current_profit < 0.08): if (current_profit < (previous_profit - 0.05)): return True, previous_sell_reason elif (0.08 <= current_profit < 0.12): if (current_profit < (previous_profit - 0.06)): return True, previous_sell_reason elif (0.12 <= current_profit): if (current_profit < (previous_profit - 0.07)): return True, previous_sell_reason else: return False, None return False, None def exit_pump_bear_signals(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: # Sell signal 1 if (last_candle['rsi_14'] > 78.0) and (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']) and (previous_candle_3['close'] > previous_candle_3['bb20_2_upp']) and (previous_candle_4['close'] > previous_candle_4['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_pump_bear_1_1_1' else: if (current_profit > 0.01): return True, 'exit_pump_bear_1_2_1' # Sell signal 2 elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_pump_bear_2_1_1' else: if (current_profit > 0.01): return True, 'exit_pump_bear_2_2_1' # Sell signal 3 elif (last_candle['rsi_14'] > 84.0) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_pump_bear_3_1_1' else: if (current_profit > 0.01): return True, 'exit_pump_bear_3_2_1' # Sell signal 4 elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_1h'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_pump_bear_4_1_1' else: if (current_profit > 0.01): return True, 'exit_pump_bear_4_2_1' # Sell signal 6 elif (last_candle['close'] < last_candle['ema_200']) and (last_candle['close'] > last_candle['ema_50']) and (last_candle['rsi_14'] > 78.5): if (current_profit > 0.01): return True, 'exit_pump_bear_6_1' # Sell signal 7 elif (last_candle['rsi_14_1h'] > 79.0) and (last_candle['crossed_below_ema_12_26']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_pump_bear_7_1_1' else: if (current_profit > 0.01): return True, 'exit_pump_bear_7_2_1' # Sell signal 8 elif (last_candle['close'] > last_candle['bb20_2_upp_1h'] * 1.07) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_pump_bear_8_1_1' else: if (current_profit > 0.01): return True, 'exit_pump_bear_8_2_1' return False, None def exit_pump_bear_main(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if (last_candle['close'] > last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 22.0): return True, 'exit_pump_bear_o_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 30.0): return True, 'exit_pump_bear_o_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 32.0): return True, 'exit_pump_bear_o_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 34.0): return True, 'exit_pump_bear_o_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 36.0): return True, 'exit_pump_bear_o_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 38.0): return True, 'exit_pump_bear_o_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 40.0): return True, 'exit_pump_bear_o_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 42.0): return True, 'exit_pump_bear_o_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 44.0): return True, 'exit_pump_bear_o_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 46.0): return True, 'exit_pump_bear_o_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 48.0): return True, 'exit_pump_bear_o_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 46.0): return True, 'exit_pump_bear_o_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 44.0): return True, 'exit_pump_bear_o_12' elif (last_candle['close'] < last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 24.0): return True, 'exit_pump_bear_u_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 32.0): return True, 'exit_pump_bear_u_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 34.0): return True, 'exit_pump_bear_u_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 36.0): return True, 'exit_pump_bear_u_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 38.0): return True, 'exit_pump_bear_u_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 40.0): return True, 'exit_pump_bear_u_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 42.0): return True, 'exit_pump_bear_u_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 44.0): return True, 'exit_pump_bear_u_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 46.0): return True, 'exit_pump_bear_u_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 48.0): return True, 'exit_pump_bear_u_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 50.0): return True, 'exit_pump_bear_u_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 48.0): return True, 'exit_pump_bear_u_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 46.0): return True, 'exit_pump_bear_u_12' return False, None def exit_pump_bear_r(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if 0.01 > current_profit >= 0.001: if (last_candle['r_480'] > -0.1): return True, 'exit_pump_bear_w_0_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bear_w_0_2' elif 0.02 > current_profit >= 0.01: if (last_candle['r_480'] > -0.2): return True, 'exit_pump_bear_w_1_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bear_w_1_2' elif 0.03 > current_profit >= 0.02: if (last_candle['r_480'] > -0.3): return True, 'exit_pump_bear_w_2_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bear_w_2_2' elif 0.04 > current_profit >= 0.03: if (last_candle['r_480'] > -0.4): return True, 'exit_pump_bear_w_3_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bear_w_3_2' elif 0.05 > current_profit >= 0.04: if (last_candle['r_480'] > -0.5): return True, 'exit_pump_bear_w_4_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bear_w_4_2' elif 0.06 > current_profit >= 0.05: if (last_candle['r_480'] > -0.6): return True, 'exit_pump_bear_w_5_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bear_w_5_2' elif 0.07 > current_profit >= 0.06: if (last_candle['r_480'] > -0.7): return True, 'exit_pump_bear_w_6_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bear_w_6_2' elif 0.08 > current_profit >= 0.07: if (last_candle['r_480'] > -0.8): return True, 'exit_pump_bear_w_7_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bear_w_7_2' elif 0.09 > current_profit >= 0.08: if (last_candle['r_480'] > -0.9): return True, 'exit_pump_bear_w_8_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bear_w_8_2' elif 0.1 > current_profit >= 0.09: if (last_candle['r_480'] > -1.0): return True, 'exit_pump_bear_w_9_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bear_w_9_2' elif 0.12 > current_profit >= 0.1: if (last_candle['r_480'] > -1.1): return True, 'exit_pump_bear_w_10_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bear_w_10_2' elif 0.2 > current_profit >= 0.12: if (last_candle['r_480'] > -0.4): return True, 'exit_pump_bear_w_11_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_pump_bear_w_11_2' elif current_profit >= 0.2: if (last_candle['r_480'] > -0.2): return True, 'exit_pump_bear_w_12_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0): return True, 'exit_pump_bear_w_12_2' return False, None def exit_pump_bear_stoploss(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: is_backtest = self.dp.runmode.value == 'backtest' # Stoploss doom if ( (self.stop_thresholds_pump[11]) and (current_profit < self.stop_thresholds_pump[1]) ): return True, 'exit_pump_bear_stoploss_doom' # Under & near EMA200, local uptrend move if ( (self.stop_thresholds_pump[13]) and (current_profit < self.stop_thresholds_pump[3]) and (last_candle['close'] < last_candle['ema_200']) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_pump[7]) and (last_candle['rsi_14'] > previous_candle_1['rsi_14']) and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_pump[9])) and (current_time - timedelta(minutes=self.stop_thresholds_pump[5]) > trade.open_date_utc) # temporary and (trade.open_date_utc.replace(tzinfo=None) >= datetime(2022, 12, 25) or is_backtest) ): return True, 'exit_pump_bear_stoploss_u_e' return False, None def exit_quick_bull(self, pair: str, current_rate: float, current_profit: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', enter_tags) -> tuple: sell = False # Original sell signals sell, signal_name = self.exit_quick_bull_signals(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Main sell signals if not sell: sell, signal_name = self.exit_quick_bull_main(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Williams %R based sells if not sell: sell, signal_name = self.exit_quick_bull_r(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Stoplosses if not sell: sell, signal_name = self.exit_quick_bull_stoploss(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Extra sell logic if not sell: if (0.09 >= current_profit > 0.02) and (last_candle['rsi_14'] > 78.0): sell, signal_name = True, 'exit_quick_bull_q_1' if (0.09 >= current_profit > 0.02) and (last_candle['cti_20'] > 0.95): sell, signal_name = True, 'exit_quick_bull_q_2' if (0.09 >= current_profit > 0.02) and (last_candle['r_14'] >= -0.1): sell, signal_name = True, 'exit_quick_bull_q_3' # Profit Target Signal # Check if pair exist on target_profit_cache if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_rate = self.target_profit_cache.data[pair]['rate'] previous_profit = self.target_profit_cache.data[pair]['profit'] previous_sell_reason = self.target_profit_cache.data[pair]['sell_reason'] previous_time_profit_reached = datetime.fromisoformat(self.target_profit_cache.data[pair]['time_profit_reached']) sell_max, signal_name_max = self.quick_bull_exit_profit_target(pair, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) if sell_max and signal_name_max is not None: return True, f"{signal_name_max}_m" if (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_quick_bull_stoploss_doom"]): # Update the target, raise it. mark_pair, mark_signal = self.quick_bull_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) # Add the pair to the list, if a sell triggered and conditions met if sell and signal_name is not None: previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if ( (previous_profit is None) or (previous_profit < current_profit) ): mark_pair, mark_signal = self.quick_bull_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" else: if ( (current_profit >= 0.01) ): previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (previous_profit is None) or (previous_profit < current_profit): mark_signal = "exit_profit_quick_bull_max" self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) if (signal_name not in ["exit_profit_quick_bull_max", "exit_quick_bull_stoploss_doom", "exit_quick_bull_stoploss_u_e"]): if sell and (signal_name is not None): return True, f"{signal_name}" return False, None def quick_bull_mark_profit_target(self, pair: str, sell: bool, signal_name: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1) -> tuple: if sell and (signal_name is not None): return pair, signal_name return None, None def quick_bull_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) -> tuple: if (previous_sell_reason in ["exit_quick_bull_stoploss_doom"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < -0.18): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.1): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.04): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason else: if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_quick_bull_stoploss_u_e"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_profit_quick_bull_max"]): if (current_profit < -0.08): # profit is under the threshold, cancel self._remove_profit_target(pair) return False, None if (0.001 <= current_profit < 0.01): if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (0.01 <= current_profit < 0.02): if (current_profit < (previous_profit - 0.02)): return True, previous_sell_reason elif (0.02 <= current_profit < 0.03): if (current_profit < (previous_profit - 0.03)): return True, previous_sell_reason elif (0.03 <= current_profit < 0.05): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (0.05 <= current_profit < 0.08): if (current_profit < (previous_profit - 0.05)): return True, previous_sell_reason elif (0.08 <= current_profit < 0.12): if (current_profit < (previous_profit - 0.06)): return True, previous_sell_reason elif (0.12 <= current_profit): if (current_profit < (previous_profit - 0.07)): return True, previous_sell_reason else: return False, None return False, None def exit_quick_bull_signals(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: # Sell signal 1 if (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']) and (previous_candle_3['close'] > previous_candle_3['bb20_2_upp']) and (previous_candle_4['close'] > previous_candle_4['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_quick_bull_1_1_1' else: if (current_profit > 0.01): return True, 'exit_quick_bull_1_2_1' # Sell signal 2 elif (last_candle['rsi_14'] > 80.0) and (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_quick_bull_2_1_1' else: if (current_profit > 0.01): return True, 'exit_quick_bull_2_2_1' # Sell signal 3 elif (last_candle['rsi_14'] > 85.0) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_quick_bull_3_1_1' else: if (current_profit > 0.01): return True, 'exit_quick_bull_3_2_1' # Sell signal 4 elif (last_candle['rsi_14'] > 80.0) and (last_candle['rsi_14_1h'] > 80.0) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_quick_bull_4_1_1' else: if (current_profit > 0.01): return True, 'exit_quick_bull_4_2_1' # Sell signal 6 elif (last_candle['close'] < last_candle['ema_200']) and (last_candle['close'] > last_candle['ema_50']) and (last_candle['rsi_14'] > 79.0): if (current_profit > 0.01): return True, 'exit_quick_bull_6_1' # Sell signal 7 elif (last_candle['rsi_14_1h'] > 79.0) and (last_candle['crossed_below_ema_12_26']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_quick_bull_7_1_1' else: if (current_profit > 0.01): return True, 'exit_quick_bull_7_2_1' # Sell signal 8 elif (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp_1h'] * 1.08): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_quick_bull_8_1_1' else: if (current_profit > 0.01): return True, 'exit_quick_bull_8_2_1' return False, None def exit_quick_bull_main(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if (last_candle['close'] > last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 20.0): return True, 'exit_quick_bull_o_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 28.0): return True, 'exit_quick_bull_o_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 30.0): return True, 'exit_quick_bull_o_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 32.0): return True, 'exit_quick_bull_o_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 34.0): return True, 'exit_quick_bull_o_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 36.0): return True, 'exit_quick_bull_o_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 38.0): return True, 'exit_quick_bull_o_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 40.0): return True, 'exit_quick_bull_o_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 42.0): return True, 'exit_quick_bull_o_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 44.0): return True, 'exit_quick_bull_o_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 46.0): return True, 'exit_quick_bull_o_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 44.0): return True, 'exit_quick_bull_o_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 42.0): return True, 'exit_quick_bull_o_12' elif (last_candle['close'] < last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 22.0): return True, 'exit_quick_bull_u_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 30.0): return True, 'exit_quick_bull_u_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 32.0): return True, 'exit_quick_bull_u_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 34.0): return True, 'exit_quick_bull_u_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 36.0): return True, 'exit_quick_bull_u_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 38.0): return True, 'exit_quick_bull_u_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 40.0): return True, 'exit_quick_bull_u_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 42.0): return True, 'exit_quick_bull_u_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 44.0): return True, 'exit_quick_bull_u_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 46.0): return True, 'exit_quick_bull_u_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 48.0): return True, 'exit_quick_bull_u_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 46.0): return True, 'exit_quick_bull_u_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 44.0): return True, 'exit_quick_bull_u_12' return False, None def exit_quick_bull_r(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if 0.01 > current_profit >= 0.001: if (last_candle['r_480'] > -0.1): return True, 'exit_quick_bull_w_0_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bull_w_0_2' elif 0.02 > current_profit >= 0.01: if (last_candle['r_480'] > -0.2): return True, 'exit_quick_bull_w_1_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bull_w_1_2' elif 0.03 > current_profit >= 0.02: if (last_candle['r_480'] > -0.3): return True, 'exit_quick_bull_w_2_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bull_w_2_2' elif 0.04 > current_profit >= 0.03: if (last_candle['r_480'] > -0.4): return True, 'exit_quick_bull_w_3_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bull_w_3_2' elif 0.05 > current_profit >= 0.04: if (last_candle['r_480'] > -0.5): return True, 'exit_quick_bull_w_4_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bull_w_4_2' elif 0.06 > current_profit >= 0.05: if (last_candle['r_480'] > -0.6): return True, 'exit_quick_bull_w_5_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bull_w_5_2' elif 0.07 > current_profit >= 0.06: if (last_candle['r_480'] > -0.7): return True, 'exit_quick_bull_w_6_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bull_w_6_2' elif 0.08 > current_profit >= 0.07: if (last_candle['r_480'] > -0.8): return True, 'exit_quick_bull_w_7_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bull_w_7_2' elif 0.09 > current_profit >= 0.08: if (last_candle['r_480'] > -0.9): return True, 'exit_quick_bull_w_8_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bull_w_8_2' elif 0.1 > current_profit >= 0.09: if (last_candle['r_480'] > -1.0): return True, 'exit_quick_bull_w_9_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bull_w_9_2' elif 0.12 > current_profit >= 0.1: if (last_candle['r_480'] > -1.1): return True, 'exit_quick_bull_w_10_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bull_w_10_2' elif 0.2 > current_profit >= 0.12: if (last_candle['r_480'] > -0.4): return True, 'exit_quick_bull_w_11_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bull_w_11_2' elif current_profit >= 0.2: if (last_candle['r_480'] > -0.2): return True, 'exit_quick_bull_w_12_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0): return True, 'exit_quick_bull_w_12_2' return False, None def exit_quick_bull_stoploss(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: is_backtest = self.dp.runmode.value == 'backtest' # Stoploss doom if ( (self.stop_thresholds_quick[10]) and (current_profit < self.stop_thresholds_quick[0]) ): return True, 'exit_quick_bull_stoploss_doom' # Under & near EMA200, local uptrend move if ( (self.stop_thresholds_quick[12]) and (current_profit < self.stop_thresholds_quick[2]) and (last_candle['close'] < last_candle['ema_200']) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_quick[6]) and (last_candle['rsi_14'] > previous_candle_1['rsi_14']) and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_quick[8])) and (current_time - timedelta(minutes=self.stop_thresholds_quick[4]) > trade.open_date_utc) # temporary and (trade.open_date_utc.replace(tzinfo=None) >= datetime(2022, 12, 25) or is_backtest) ): return True, 'exit_quick_bull_stoploss_u_e' return False, None def exit_quick_bear(self, pair: str, current_rate: float, current_profit: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', enter_tags) -> tuple: sell = False # Original sell signals sell, signal_name = self.exit_quick_bear_signals(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Main sell signals if not sell: sell, signal_name = self.exit_quick_bear_main(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Williams %R based sells if not sell: sell, signal_name = self.exit_quick_bear_r(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Stoplosses if not sell: sell, signal_name = self.exit_quick_bear_stoploss(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Extra sell logic if not sell: if (0.09 >= current_profit > 0.02) and (last_candle['rsi_14'] > 78.0): sell, signal_name = True, 'exit_quick_bear_q_1' if (0.09 >= current_profit > 0.02) and (last_candle['cti_20'] > 0.95): sell, signal_name = True, 'exit_quick_bear_q_2' if (0.09 >= current_profit > 0.02) and (last_candle['r_14'] >= -0.1): sell, signal_name = True, 'exit_quick_bear_q_3' # Profit Target Signal # Check if pair exist on target_profit_cache if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_rate = self.target_profit_cache.data[pair]['rate'] previous_profit = self.target_profit_cache.data[pair]['profit'] previous_sell_reason = self.target_profit_cache.data[pair]['sell_reason'] previous_time_profit_reached = datetime.fromisoformat(self.target_profit_cache.data[pair]['time_profit_reached']) sell_max, signal_name_max = self.quick_bear_exit_profit_target(pair, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) if sell_max and signal_name_max is not None: return True, f"{signal_name_max}_m" if (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_quick_bear_stoploss_doom"]): # Update the target, raise it. mark_pair, mark_signal = self.quick_bear_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) # Add the pair to the list, if a sell triggered and conditions met if sell and signal_name is not None: previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if ( (previous_profit is None) or (previous_profit < current_profit) ): mark_pair, mark_signal = self.quick_bear_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" else: if ( (current_profit >= 0.01) ): previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (previous_profit is None) or (previous_profit < current_profit): mark_signal = "exit_profit_quick_bear_max" self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) if (signal_name not in ["exit_profit_quick_bear_max", "exit_quick_bear_stoploss_doom", "exit_quick_bear_stoploss_u_e"]): if sell and (signal_name is not None): return True, f"{signal_name}" return False, None def quick_bear_mark_profit_target(self, pair: str, sell: bool, signal_name: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1) -> tuple: if sell and (signal_name is not None): return pair, signal_name return None, None def quick_bear_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) -> tuple: if (previous_sell_reason in ["exit_quick_bear_stoploss_doom"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < -0.18): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.1): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.04): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason else: if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_quick_bear_stoploss_u_e"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_profit_quick_bear_max"]): if (current_profit < -0.08): # profit is under the threshold, cancel self._remove_profit_target(pair) return False, None if (0.001 <= current_profit < 0.01): if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (0.01 <= current_profit < 0.02): if (current_profit < (previous_profit - 0.02)): return True, previous_sell_reason elif (0.02 <= current_profit < 0.03): if (current_profit < (previous_profit - 0.03)): return True, previous_sell_reason elif (0.03 <= current_profit < 0.05): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (0.05 <= current_profit < 0.08): if (current_profit < (previous_profit - 0.05)): return True, previous_sell_reason elif (0.08 <= current_profit < 0.12): if (current_profit < (previous_profit - 0.06)): return True, previous_sell_reason elif (0.12 <= current_profit): if (current_profit < (previous_profit - 0.07)): return True, previous_sell_reason else: return False, None return False, None def exit_quick_bear_signals(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: # Sell signal 1 if (last_candle['rsi_14'] > 78.0) and (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']) and (previous_candle_3['close'] > previous_candle_3['bb20_2_upp']) and (previous_candle_4['close'] > previous_candle_4['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_quick_bear_1_1_1' else: if (current_profit > 0.01): return True, 'exit_quick_bear_1_2_1' # Sell signal 2 elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_quick_bear_2_1_1' else: if (current_profit > 0.01): return True, 'exit_quick_bear_2_2_1' # Sell signal 3 elif (last_candle['rsi_14'] > 84.0) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_quick_bear_3_1_1' else: if (current_profit > 0.01): return True, 'exit_quick_bear_3_2_1' # Sell signal 4 elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_1h'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_quick_bear_4_1_1' else: if (current_profit > 0.01): return True, 'exit_quick_bear_4_2_1' # Sell signal 6 elif (last_candle['close'] < last_candle['ema_200']) and (last_candle['close'] > last_candle['ema_50']) and (last_candle['rsi_14'] > 78.5): if (current_profit > 0.01): return True, 'exit_quick_bear_6_1' # Sell signal 7 elif (last_candle['rsi_14_1h'] > 79.0) and (last_candle['crossed_below_ema_12_26']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_quick_bear_7_1_1' else: if (current_profit > 0.01): return True, 'exit_quick_bear_7_2_1' # Sell signal 8 elif (last_candle['close'] > last_candle['bb20_2_upp_1h'] * 1.07) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_quick_bear_8_1_1' else: if (current_profit > 0.01): return True, 'exit_quick_bear_8_2_1' return False, None def exit_quick_bear_main(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if (last_candle['close'] > last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 22.0): return True, 'exit_quick_bear_o_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 30.0): return True, 'exit_quick_bear_o_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 32.0): return True, 'exit_quick_bear_o_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 34.0): return True, 'exit_quick_bear_o_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 36.0): return True, 'exit_quick_bear_o_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 38.0): return True, 'exit_quick_bear_o_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 40.0): return True, 'exit_quick_bear_o_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 42.0): return True, 'exit_quick_bear_o_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 44.0): return True, 'exit_quick_bear_o_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 46.0): return True, 'exit_quick_bear_o_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 48.0): return True, 'exit_quick_bear_o_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 46.0): return True, 'exit_quick_bear_o_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 44.0): return True, 'exit_quick_bear_o_12' elif (last_candle['close'] < last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 24.0): return True, 'exit_quick_bear_u_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 32.0): return True, 'exit_quick_bear_u_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 34.0): return True, 'exit_quick_bear_u_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 36.0): return True, 'exit_quick_bear_u_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 38.0): return True, 'exit_quick_bear_u_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 40.0): return True, 'exit_quick_bear_u_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 42.0): return True, 'exit_quick_bear_u_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 44.0): return True, 'exit_quick_bear_u_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 46.0): return True, 'exit_quick_bear_u_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 48.0): return True, 'exit_quick_bear_u_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 50.0): return True, 'exit_quick_bear_u_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 48.0): return True, 'exit_quick_bear_u_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 46.0): return True, 'exit_quick_bear_u_12' return False, None def exit_quick_bear_r(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if 0.01 > current_profit >= 0.001: if (last_candle['r_480'] > -0.1): return True, 'exit_quick_bear_w_0_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bear_w_0_2' elif 0.02 > current_profit >= 0.01: if (last_candle['r_480'] > -0.2): return True, 'exit_quick_bear_w_1_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bear_w_1_2' elif 0.03 > current_profit >= 0.02: if (last_candle['r_480'] > -0.3): return True, 'exit_quick_bear_w_2_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bear_w_2_2' elif 0.04 > current_profit >= 0.03: if (last_candle['r_480'] > -0.4): return True, 'exit_quick_bear_w_3_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bear_w_3_2' elif 0.05 > current_profit >= 0.04: if (last_candle['r_480'] > -0.5): return True, 'exit_quick_bear_w_4_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bear_w_4_2' elif 0.06 > current_profit >= 0.05: if (last_candle['r_480'] > -0.6): return True, 'exit_quick_bear_w_5_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bear_w_5_2' elif 0.07 > current_profit >= 0.06: if (last_candle['r_480'] > -0.7): return True, 'exit_quick_bear_w_6_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bear_w_6_2' elif 0.08 > current_profit >= 0.07: if (last_candle['r_480'] > -0.8): return True, 'exit_quick_bear_w_7_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bear_w_7_2' elif 0.09 > current_profit >= 0.08: if (last_candle['r_480'] > -0.9): return True, 'exit_quick_bear_w_8_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bear_w_8_2' elif 0.1 > current_profit >= 0.09: if (last_candle['r_480'] > -1.0): return True, 'exit_quick_bear_w_9_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bear_w_9_2' elif 0.12 > current_profit >= 0.1: if (last_candle['r_480'] > -1.1): return True, 'exit_quick_bear_w_10_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bear_w_10_2' elif 0.2 > current_profit >= 0.12: if (last_candle['r_480'] > -0.4): return True, 'exit_quick_bear_w_11_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_quick_bear_w_11_2' elif current_profit >= 0.2: if (last_candle['r_480'] > -0.2): return True, 'exit_quick_bear_w_12_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0): return True, 'exit_quick_bear_w_12_2' return False, None def exit_quick_bear_stoploss(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: is_backtest = self.dp.runmode.value == 'backtest' # Stoploss doom if ( (self.stop_thresholds_quick[11]) and (current_profit < self.stop_thresholds_quick[1]) ): return True, 'exit_quick_bear_stoploss_doom' # Under & near EMA200, local uptrend move if ( (self.stop_thresholds_quick[13]) and (current_profit < self.stop_thresholds_quick[3]) and (last_candle['close'] < last_candle['ema_200']) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_quick[7]) and (last_candle['rsi_14'] > previous_candle_1['rsi_14']) and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_quick[9])) and (current_time - timedelta(minutes=self.stop_thresholds_quick[5]) > trade.open_date_utc) # temporary and (trade.open_date_utc.replace(tzinfo=None) >= datetime(2022, 12, 25) or is_backtest) ): return True, 'exit_quick_bear_stoploss_u_e' return False, None def exit_rebuy_bull(self, pair: str, current_rate: float, current_profit: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', enter_tags) -> tuple: sell = False # Original sell signals sell, signal_name = self.exit_rebuy_bull_signals(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Main sell signals if not sell: sell, signal_name = self.exit_rebuy_bull_main(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Williams %R based sells if not sell: sell, signal_name = self.exit_rebuy_bull_r(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Stoplosses if not sell: sell, signal_name = self.exit_rebuy_bull_stoploss(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Profit Target Signal # Check if pair exist on target_profit_cache if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_rate = self.target_profit_cache.data[pair]['rate'] previous_profit = self.target_profit_cache.data[pair]['profit'] previous_sell_reason = self.target_profit_cache.data[pair]['sell_reason'] previous_time_profit_reached = datetime.fromisoformat(self.target_profit_cache.data[pair]['time_profit_reached']) sell_max, signal_name_max = self.rebuy_bull_exit_profit_target(pair, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) if sell_max and signal_name_max is not None: return True, f"{signal_name_max}_m" if (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_rebuy_bull_stoploss_doom"]): # Update the target, raise it. mark_pair, mark_signal = self.rebuy_bull_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) # Add the pair to the list, if a sell triggered and conditions met if sell and signal_name is not None: previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if ( (previous_profit is None) or (previous_profit < current_profit) ): mark_pair, mark_signal = self.rebuy_bull_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" else: if ( (current_profit >= 0.01) ): previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (previous_profit is None) or (previous_profit < current_profit): mark_signal = "exit_profit_rebuy_bull_max" self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) if (signal_name not in ["exit_profit_rebuy_bull_max", "exit_rebuy_bull_stoploss_doom", "exit_rebuy_bull_stoploss_u_e"]): if sell and (signal_name is not None): return True, f"{signal_name}" return False, None def rebuy_bull_mark_profit_target(self, pair: str, sell: bool, signal_name: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1) -> tuple: if sell and (signal_name is not None): return pair, signal_name return None, None def rebuy_bull_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) -> tuple: if (previous_sell_reason in ["exit_rebuy_bull_stoploss_doom"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < -0.18): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.1): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.04): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason else: if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_rebuy_bull_stoploss_u_e"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_profit_rebuy_bull_max"]): if (current_profit < -0.08): # profit is under the threshold, cancel self._remove_profit_target(pair) return False, None if (0.001 <= current_profit < 0.01): if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (0.01 <= current_profit < 0.02): if (current_profit < (previous_profit - 0.02)): return True, previous_sell_reason elif (0.02 <= current_profit < 0.03): if (current_profit < (previous_profit - 0.03)): return True, previous_sell_reason elif (0.03 <= current_profit < 0.05): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (0.05 <= current_profit < 0.08): if (current_profit < (previous_profit - 0.05)): return True, previous_sell_reason elif (0.08 <= current_profit < 0.12): if (current_profit < (previous_profit - 0.06)): return True, previous_sell_reason elif (0.12 <= current_profit): if (current_profit < (previous_profit - 0.07)): return True, previous_sell_reason else: return False, None return False, None def exit_rebuy_bull_signals(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: # Sell signal 1 if (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']) and (previous_candle_3['close'] > previous_candle_3['bb20_2_upp']) and (previous_candle_4['close'] > previous_candle_4['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_rebuy_bull_1_1_1' else: if (current_profit > 0.01): return True, 'exit_rebuy_bull_1_2_1' # Sell signal 2 elif (last_candle['rsi_14'] > 80.0) and (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_rebuy_bull_2_1_1' else: if (current_profit > 0.01): return True, 'exit_rebuy_bull_2_2_1' # Sell signal 3 elif (last_candle['rsi_14'] > 85.0) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_rebuy_bull_3_1_1' else: if (current_profit > 0.01): return True, 'exit_rebuy_bull_3_2_1' # Sell signal 4 elif (last_candle['rsi_14'] > 80.0) and (last_candle['rsi_14_1h'] > 80.0) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_rebuy_bull_4_1_1' else: if (current_profit > 0.01): return True, 'exit_rebuy_bull_4_2_1' # Sell signal 6 elif (last_candle['close'] < last_candle['ema_200']) and (last_candle['close'] > last_candle['ema_50']) and (last_candle['rsi_14'] > 79.0): if (current_profit > 0.01): return True, 'exit_rebuy_bull_6_1' # Sell signal 7 elif (last_candle['rsi_14_1h'] > 79.0) and (last_candle['crossed_below_ema_12_26']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_rebuy_bull_7_1_1' else: if (current_profit > 0.01): return True, 'exit_rebuy_bull_7_2_1' # Sell signal 8 elif (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp_1h'] * 1.08): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_rebuy_bull_8_1_1' else: if (current_profit > 0.01): return True, 'exit_rebuy_bull_8_2_1' return False, None def exit_rebuy_bull_main(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if (last_candle['close'] > last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 20.0): return True, 'exit_rebuy_bull_o_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 28.0): return True, 'exit_rebuy_bull_o_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 30.0): return True, 'exit_rebuy_bull_o_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 32.0): return True, 'exit_rebuy_bull_o_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 34.0): return True, 'exit_rebuy_bull_o_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 36.0): return True, 'exit_rebuy_bull_o_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 38.0): return True, 'exit_rebuy_bull_o_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 40.0): return True, 'exit_rebuy_bull_o_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 42.0): return True, 'exit_rebuy_bull_o_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 44.0): return True, 'exit_rebuy_bull_o_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 46.0): return True, 'exit_rebuy_bull_o_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 44.0): return True, 'exit_rebuy_bull_o_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 42.0): return True, 'exit_rebuy_bull_o_12' elif (last_candle['close'] < last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 22.0): return True, 'exit_rebuy_bull_u_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 30.0): return True, 'exit_rebuy_bull_u_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 32.0): return True, 'exit_rebuy_bull_u_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 34.0): return True, 'exit_rebuy_bull_u_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 36.0): return True, 'exit_rebuy_bull_u_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 38.0): return True, 'exit_rebuy_bull_u_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 40.0): return True, 'exit_rebuy_bull_u_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 42.0): return True, 'exit_rebuy_bull_u_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 44.0): return True, 'exit_rebuy_bull_u_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 46.0): return True, 'exit_rebuy_bull_u_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 48.0): return True, 'exit_rebuy_bull_u_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 46.0): return True, 'exit_rebuy_bull_u_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 44.0): return True, 'exit_rebuy_bull_u_12' return False, None def exit_rebuy_bull_r(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if 0.01 > current_profit >= 0.001: if (last_candle['r_480'] > -0.1): return True, 'exit_rebuy_bull_w_0_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bull_w_0_2' elif 0.02 > current_profit >= 0.01: if (last_candle['r_480'] > -0.2): return True, 'exit_rebuy_bull_w_1_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bull_w_1_2' elif 0.03 > current_profit >= 0.02: if (last_candle['r_480'] > -0.3): return True, 'exit_rebuy_bull_w_2_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bull_w_2_2' elif 0.04 > current_profit >= 0.03: if (last_candle['r_480'] > -0.4): return True, 'exit_rebuy_bull_w_3_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bull_w_3_2' elif 0.05 > current_profit >= 0.04: if (last_candle['r_480'] > -0.5): return True, 'exit_rebuy_bull_w_4_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bull_w_4_2' elif 0.06 > current_profit >= 0.05: if (last_candle['r_480'] > -0.6): return True, 'exit_rebuy_bull_w_5_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bull_w_5_2' elif 0.07 > current_profit >= 0.06: if (last_candle['r_480'] > -0.7): return True, 'exit_rebuy_bull_w_6_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bull_w_6_2' elif 0.08 > current_profit >= 0.07: if (last_candle['r_480'] > -0.8): return True, 'exit_rebuy_bull_w_7_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bull_w_7_2' elif 0.09 > current_profit >= 0.08: if (last_candle['r_480'] > -0.9): return True, 'exit_rebuy_bull_w_8_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bull_w_8_2' elif 0.1 > current_profit >= 0.09: if (last_candle['r_480'] > -1.0): return True, 'exit_rebuy_bull_w_9_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bull_w_9_2' elif 0.12 > current_profit >= 0.1: if (last_candle['r_480'] > -1.1): return True, 'exit_rebuy_bull_w_10_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bull_w_10_2' elif 0.2 > current_profit >= 0.12: if (last_candle['r_480'] > -0.4): return True, 'exit_rebuy_bull_w_11_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bull_w_11_2' elif current_profit >= 0.2: if (last_candle['r_480'] > -0.2): return True, 'exit_rebuy_bull_w_12_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0): return True, 'exit_rebuy_bull_w_12_2' return False, None def exit_rebuy_bull_stoploss(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: # Stoploss doom if ( (self.stop_thresholds_rebuy[10]) and (current_profit < self.stop_thresholds_rebuy[0]) ): return True, 'exit_rebuy_bull_stoploss_doom' # Under & near EMA200, local uptrend move if ( (self.stop_thresholds_rebuy[12]) and (current_profit < self.stop_thresholds_rebuy[2]) and (last_candle['close'] < last_candle['ema_200']) #and (last_candle['cmf_20'] < -0.0) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_rebuy[6]) and (last_candle['rsi_14'] > previous_candle_1['rsi_14']) and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_rebuy[8])) and (current_time - timedelta(minutes=self.stop_thresholds_rebuy[4]) > trade.open_date_utc) ): return True, 'exit_rebuy_bull_stoploss_u_e' return False, None def exit_rebuy_bear(self, pair: str, current_rate: float, current_profit: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', enter_tags) -> tuple: sell = False # Original sell signals sell, signal_name = self.exit_rebuy_bear_signals(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Main sell signals if not sell: sell, signal_name = self.exit_rebuy_bear_main(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Williams %R based sells if not sell: sell, signal_name = self.exit_rebuy_bear_r(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Stoplosses if not sell: sell, signal_name = self.exit_rebuy_bear_stoploss(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Profit Target Signal # Check if pair exist on target_profit_cache if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_rate = self.target_profit_cache.data[pair]['rate'] previous_profit = self.target_profit_cache.data[pair]['profit'] previous_sell_reason = self.target_profit_cache.data[pair]['sell_reason'] previous_time_profit_reached = datetime.fromisoformat(self.target_profit_cache.data[pair]['time_profit_reached']) sell_max, signal_name_max = self.rebuy_bear_exit_profit_target(pair, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) if sell_max and signal_name_max is not None: return True, f"{signal_name_max}_m" if (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_rebuy_bear_stoploss_doom"]): # Update the target, raise it. mark_pair, mark_signal = self.rebuy_bear_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) # Add the pair to the list, if a sell triggered and conditions met if sell and signal_name is not None: previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if ( (previous_profit is None) or (previous_profit < current_profit) ): mark_pair, mark_signal = self.rebuy_bear_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" else: if ( (current_profit >= 0.01) ): previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (previous_profit is None) or (previous_profit < current_profit): mark_signal = "exit_profit_rebuy_bear_max" self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) if (signal_name not in ["exit_profit_rebuy_bear_max", "exit_rebuy_bear_stoploss_doom", "exit_rebuy_bear_stoploss_u_e"]): if sell and (signal_name is not None): return True, f"{signal_name}" return False, None def rebuy_bear_mark_profit_target(self, pair: str, sell: bool, signal_name: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1) -> tuple: if sell and (signal_name is not None): return pair, signal_name return None, None def rebuy_bear_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) -> tuple: if (previous_sell_reason in ["exit_rebuy_bear_stoploss_doom"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < -0.18): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.1): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.04): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason else: if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_rebuy_bear_stoploss_u_e"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_profit_rebuy_bear_max"]): if (current_profit < -0.08): # profit is under the threshold, cancel self._remove_profit_target(pair) return False, None if (0.001 <= current_profit < 0.01): if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (0.01 <= current_profit < 0.02): if (current_profit < (previous_profit - 0.02)): return True, previous_sell_reason elif (0.02 <= current_profit < 0.03): if (current_profit < (previous_profit - 0.03)): return True, previous_sell_reason elif (0.03 <= current_profit < 0.05): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (0.05 <= current_profit < 0.08): if (current_profit < (previous_profit - 0.05)): return True, previous_sell_reason elif (0.08 <= current_profit < 0.12): if (current_profit < (previous_profit - 0.06)): return True, previous_sell_reason elif (0.12 <= current_profit): if (current_profit < (previous_profit - 0.07)): return True, previous_sell_reason else: return False, None return False, None def exit_rebuy_bear_signals(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: # Sell signal 1 if (last_candle['rsi_14'] > 78.0) and (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']) and (previous_candle_3['close'] > previous_candle_3['bb20_2_upp']) and (previous_candle_4['close'] > previous_candle_4['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_rebuy_bear_1_1_1' else: if (current_profit > 0.01): return True, 'exit_rebuy_bear_1_2_1' # Sell signal 2 elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_rebuy_bear_2_1_1' else: if (current_profit > 0.01): return True, 'exit_rebuy_bear_2_2_1' # Sell signal 3 elif (last_candle['rsi_14'] > 84.0) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_rebuy_bear_3_1_1' else: if (current_profit > 0.01): return True, 'exit_rebuy_bear_3_2_1' # Sell signal 4 elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_1h'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_rebuy_bear_4_1_1' else: if (current_profit > 0.01): return True, 'exit_rebuy_bear_4_2_1' # Sell signal 6 elif (last_candle['close'] < last_candle['ema_200']) and (last_candle['close'] > last_candle['ema_50']) and (last_candle['rsi_14'] > 78.5): if (current_profit > 0.01): return True, 'exit_rebuy_bear_6_1' # Sell signal 7 elif (last_candle['rsi_14_1h'] > 79.0) and (last_candle['crossed_below_ema_12_26']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_rebuy_bear_7_1_1' else: if (current_profit > 0.01): return True, 'exit_rebuy_bear_7_2_1' # Sell signal 8 elif (last_candle['close'] > last_candle['bb20_2_upp_1h'] * 1.07) and (last_candle['rsi_14_4h'] > 75.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_rebuy_bear_8_1_1' else: if (current_profit > 0.01): return True, 'exit_rebuy_bear_8_2_1' return False, None def exit_rebuy_bear_main(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if (last_candle['close'] > last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 22.0): return True, 'exit_rebuy_bear_o_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 30.0): return True, 'exit_rebuy_bear_o_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 32.0): return True, 'exit_rebuy_bear_o_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 34.0): return True, 'exit_rebuy_bear_o_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 36.0): return True, 'exit_rebuy_bear_o_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 38.0): return True, 'exit_rebuy_bear_o_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 40.0): return True, 'exit_rebuy_bear_o_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 42.0): return True, 'exit_rebuy_bear_o_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 44.0): return True, 'exit_rebuy_bear_o_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 46.0): return True, 'exit_rebuy_bear_o_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 48.0): return True, 'exit_rebuy_bear_o_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 46.0): return True, 'exit_rebuy_bear_o_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 44.0): return True, 'exit_rebuy_bear_o_12' elif (last_candle['close'] < last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 24.0): return True, 'exit_rebuy_bear_u_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 32.0): return True, 'exit_rebuy_bear_u_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 34.0): return True, 'exit_rebuy_bear_u_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 36.0): return True, 'exit_rebuy_bear_u_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 38.0): return True, 'exit_rebuy_bear_u_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 40.0): return True, 'exit_rebuy_bear_u_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 42.0): return True, 'exit_rebuy_bear_u_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 44.0): return True, 'exit_rebuy_bear_u_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 46.0): return True, 'exit_rebuy_bear_u_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 48.0): return True, 'exit_rebuy_bear_u_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 50.0): return True, 'exit_rebuy_bear_u_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 48.0): return True, 'exit_rebuy_bear_u_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 46.0): return True, 'exit_rebuy_bear_u_12' return False, None def exit_rebuy_bear_r(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if 0.01 > current_profit >= 0.001: if (last_candle['r_480'] > -0.1): return True, 'exit_rebuy_bear_w_0_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bear_w_0_2' elif 0.02 > current_profit >= 0.01: if (last_candle['r_480'] > -0.2): return True, 'exit_rebuy_bear_w_1_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bear_w_1_2' elif 0.03 > current_profit >= 0.02: if (last_candle['r_480'] > -0.3): return True, 'exit_rebuy_bear_w_2_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bear_w_2_2' elif 0.04 > current_profit >= 0.03: if (last_candle['r_480'] > -0.4): return True, 'exit_rebuy_bear_w_3_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bear_w_3_2' elif 0.05 > current_profit >= 0.04: if (last_candle['r_480'] > -0.5): return True, 'exit_rebuy_bear_w_4_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bear_w_4_2' elif 0.06 > current_profit >= 0.05: if (last_candle['r_480'] > -0.6): return True, 'exit_rebuy_bear_w_5_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bear_w_5_2' elif 0.07 > current_profit >= 0.06: if (last_candle['r_480'] > -0.7): return True, 'exit_rebuy_bear_w_6_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bear_w_6_2' elif 0.08 > current_profit >= 0.07: if (last_candle['r_480'] > -0.8): return True, 'exit_rebuy_bear_w_7_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bear_w_7_2' elif 0.09 > current_profit >= 0.08: if (last_candle['r_480'] > -0.9): return True, 'exit_rebuy_bear_w_8_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bear_w_8_2' elif 0.1 > current_profit >= 0.09: if (last_candle['r_480'] > -1.0): return True, 'exit_rebuy_bear_w_9_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bear_w_9_2' elif 0.12 > current_profit >= 0.1: if (last_candle['r_480'] > -1.1): return True, 'exit_rebuy_bear_w_10_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bear_w_10_2' elif 0.2 > current_profit >= 0.12: if (last_candle['r_480'] > -0.4): return True, 'exit_rebuy_bear_w_11_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_rebuy_bear_w_11_2' elif current_profit >= 0.2: if (last_candle['r_480'] > -0.2): return True, 'exit_rebuy_bear_w_12_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0): return True, 'exit_rebuy_bear_w_12_2' return False, None def exit_rebuy_bear_stoploss(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: # Stoploss doom if ( (self.stop_thresholds_rebuy[11]) and (current_profit < self.stop_thresholds_rebuy[1]) ): return True, 'exit_rebuy_bear_stoploss_doom' # Under & near EMA200, local uptrend move if ( (self.stop_thresholds_rebuy[13]) and (current_profit < self.stop_thresholds_rebuy[3]) and (last_candle['close'] < last_candle['ema_200']) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_rebuy[7]) and (last_candle['rsi_14'] > previous_candle_1['rsi_14']) and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_rebuy[9])) and (current_time - timedelta(minutes=self.stop_thresholds_rebuy[5]) > trade.open_date_utc) ): return True, 'exit_rebuy_bear_stoploss_u_e' return False, None def exit_long_bull(self, pair: str, current_rate: float, current_profit: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', enter_tags) -> tuple: sell = False # Original sell signals sell, signal_name = self.exit_long_bull_signals(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Main sell signals if not sell: sell, signal_name = self.exit_long_bull_main(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Williams %R based sells if not sell: sell, signal_name = self.exit_long_bull_r(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Stoplosses if not sell: sell, signal_name = self.exit_long_bull_stoploss(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Profit Target Signal # Check if pair exist on target_profit_cache if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_rate = self.target_profit_cache.data[pair]['rate'] previous_profit = self.target_profit_cache.data[pair]['profit'] previous_sell_reason = self.target_profit_cache.data[pair]['sell_reason'] previous_time_profit_reached = datetime.fromisoformat(self.target_profit_cache.data[pair]['time_profit_reached']) sell_max, signal_name_max = self.long_bull_exit_profit_target(pair, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) if sell_max and signal_name_max is not None: return True, f"{signal_name_max}_m" if (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_long_bull_stoploss_doom"]): # Update the target, raise it. mark_pair, mark_signal = self.long_bull_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) # Add the pair to the list, if a sell triggered and conditions met if sell and signal_name is not None: previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if ( (previous_profit is None) or (previous_profit < current_profit) ): mark_pair, mark_signal = self.long_bull_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" else: if ( (current_profit >= 0.05) ): previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (previous_profit is None) or (previous_profit < current_profit): mark_signal = "exit_profit_long_bull_max" self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) if (signal_name not in ["exit_profit_long_bull_max", "exit_long_bull_stoploss_doom", "exit_long_bull_stoploss_u_e"]): if sell and (signal_name is not None): return True, f"{signal_name}" return False, None def long_bull_mark_profit_target(self, pair: str, sell: bool, signal_name: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1) -> tuple: if sell and (signal_name is not None): return pair, signal_name return None, None def long_bull_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) -> tuple: if (previous_sell_reason in ["exit_long_bull_stoploss_doom"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < -0.18): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.1): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.04): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason else: if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_long_bull_stoploss_u_e"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_profit_long_bull_max"]): if (current_profit < -0.08): # profit is under the threshold, cancel it self._remove_profit_target(pair) return False, None if (0.001 <= current_profit < 0.01): if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (0.01 <= current_profit < 0.02): if (current_profit < (previous_profit - 0.02)): return True, previous_sell_reason elif (0.02 <= current_profit < 0.03): if (current_profit < (previous_profit - 0.03)): return True, previous_sell_reason elif (0.03 <= current_profit < 0.05): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (0.05 <= current_profit < 0.08): if (current_profit < (previous_profit - 0.05)): return True, previous_sell_reason elif (0.08 <= current_profit < 0.12): if (current_profit < (previous_profit - 0.06)): return True, previous_sell_reason elif (0.12 <= current_profit): if (current_profit < (previous_profit - 0.07)): return True, previous_sell_reason else: return False, None return False, None def exit_long_bull_signals(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: # Sell signal 1 if (last_candle['rsi_14'] > 78.0) and (last_candle['rsi_14_4h'] > 50.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']) and (previous_candle_3['close'] > previous_candle_3['bb20_2_upp']) and (previous_candle_4['close'] > previous_candle_4['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_long_bull_1_1_1' else: if (current_profit > 0.01): return True, 'exit_long_bull_1_2_1' # Sell signal 2 elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_4h'] > 50.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_long_bull_2_1_1' else: if (current_profit > 0.01): return True, 'exit_long_bull_2_2_1' # Sell signal 3 elif (last_candle['rsi_14'] > 81.0) and (last_candle['rsi_14_4h'] > 50.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_long_bull_3_1_1' else: if (current_profit > 0.01): return True, 'exit_long_bull_3_2_1' # Sell signal 4 elif (last_candle['rsi_14'] > 80.0) and (last_candle['rsi_14_1h'] > 80.0) and (last_candle['rsi_14_4h'] > 50.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_long_bull_4_1_1' else: if (current_profit > 0.01): return True, 'exit_long_bull_4_2_1' # Sell signal 6 elif (last_candle['close'] < last_candle['ema_200']) and (last_candle['close'] > last_candle['ema_50']) and (last_candle['rsi_14'] > 79.0): if (current_profit > 0.01): return True, 'exit_long_bull_6_1' # Sell signal 7 elif (last_candle['rsi_14_1h'] > 79.0) and (last_candle['crossed_below_ema_12_26']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_long_bull_7_1_1' else: if (current_profit > 0.01): return True, 'exit_long_bull_7_2_1' # Sell signal 8 elif (last_candle['rsi_14_4h'] > 50.0) and (last_candle['close'] > last_candle['bb20_2_upp_1h'] * 1.08): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_long_bull_8_1_1' else: if (current_profit > 0.01): return True, 'exit_long_bull_8_2_1' return False, None def exit_long_bull_main(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if (last_candle['close'] > last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 10.0): return True, 'exit_long_bull_o_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 28.0): return True, 'exit_long_bull_o_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 30.0): return True, 'exit_long_bull_o_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 32.0): return True, 'exit_long_bull_o_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 34.0): return True, 'exit_long_bull_o_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 36.0): return True, 'exit_long_bull_o_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 38.0): return True, 'exit_long_bull_o_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 40.0): return True, 'exit_long_bull_o_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 42.0): return True, 'exit_long_bull_o_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 44.0): return True, 'exit_long_bull_o_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 46.0): return True, 'exit_long_bull_o_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 44.0): return True, 'exit_long_bull_o_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 42.0): return True, 'exit_long_bull_o_12' elif (last_candle['close'] < last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 12.0): return True, 'exit_long_bull_u_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 30.0): return True, 'exit_long_bull_u_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 32.0): return True, 'exit_long_bull_u_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 34.0): return True, 'exit_long_bull_u_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 36.0): return True, 'exit_long_bull_u_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 38.0): return True, 'exit_long_bull_u_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 40.0): return True, 'exit_long_bull_u_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 42.0): return True, 'exit_long_bull_u_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 44.0): return True, 'exit_long_bull_u_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 46.0): return True, 'exit_long_bull_u_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 48.0): return True, 'exit_long_bull_u_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 46.0): return True, 'exit_long_bull_u_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 44.0): return True, 'exit_long_bull_u_12' return False, None def exit_long_bull_r(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if 0.01 > current_profit >= 0.001: if (last_candle['r_480'] > -0.1): return True, 'exit_long_bull_w_0_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bull_w_0_2' elif 0.02 > current_profit >= 0.01: if (last_candle['r_480'] > -0.2): return True, 'exit_long_bull_w_1_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bull_w_1_2' elif 0.03 > current_profit >= 0.02: if (last_candle['r_480'] > -0.3): return True, 'exit_long_bull_w_2_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bull_w_2_2' elif 0.04 > current_profit >= 0.03: if (last_candle['r_480'] > -0.4): return True, 'exit_long_bull_w_3_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bull_w_3_2' elif 0.05 > current_profit >= 0.04: if (last_candle['r_480'] > -0.5): return True, 'exit_long_bull_w_4_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bull_w_4_2' elif 0.06 > current_profit >= 0.05: if (last_candle['r_480'] > -0.6): return True, 'exit_long_bull_w_5_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bull_w_5_2' elif 0.07 > current_profit >= 0.06: if (last_candle['r_480'] > -0.7): return True, 'exit_long_bull_w_6_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bull_w_6_2' elif 0.08 > current_profit >= 0.07: if (last_candle['r_480'] > -0.8): return True, 'exit_long_bull_w_7_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bull_w_7_2' elif 0.09 > current_profit >= 0.08: if (last_candle['r_480'] > -0.9): return True, 'exit_long_bull_w_8_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bull_w_8_2' elif 0.1 > current_profit >= 0.09: if (last_candle['r_480'] > -1.0): return True, 'exit_long_bull_w_9_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bull_w_9_2' elif 0.12 > current_profit >= 0.1: if (last_candle['r_480'] > -1.1): return True, 'exit_long_bull_w_10_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bull_w_10_2' elif 0.2 > current_profit >= 0.12: if (last_candle['r_480'] > -0.4): return True, 'exit_long_bull_w_11_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bull_w_11_2' elif current_profit >= 0.2: if (last_candle['r_480'] > -0.2): return True, 'exit_long_bull_w_12_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0): return True, 'exit_long_bull_w_12_2' return False, None def exit_long_bull_stoploss(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: # Stoploss doom if ( (self.stop_thresholds_long[10]) and (current_profit < self.stop_thresholds_long[0]) ): return True, 'exit_long_bull_stoploss_doom' # Under & near EMA200, local uptrend move if ( (self.stop_thresholds_long[12]) and (current_profit < self.stop_thresholds_long[2]) and (last_candle['close'] < last_candle['ema_200']) #and (last_candle['cmf_20'] < -0.0) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_long[6]) and (last_candle['rsi_14'] > previous_candle_1['rsi_14']) and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_long[8])) and (current_time - timedelta(minutes=self.stop_thresholds_long[4]) > trade.open_date_utc) ): return True, 'exit_long_bull_stoploss_u_e' return False, None def exit_long_bear(self, pair: str, current_rate: float, current_profit: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', enter_tags) -> tuple: sell = False # Original sell signals sell, signal_name = self.exit_long_bear_signals(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Main sell signals if not sell: sell, signal_name = self.exit_long_bear_main(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Williams %R based sells if not sell: sell, signal_name = self.exit_long_bear_r(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Stoplosses if not sell: sell, signal_name = self.exit_long_bear_stoploss(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Profit Target Signal # Check if pair exist on target_profit_cache if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_rate = self.target_profit_cache.data[pair]['rate'] previous_profit = self.target_profit_cache.data[pair]['profit'] previous_sell_reason = self.target_profit_cache.data[pair]['sell_reason'] previous_time_profit_reached = datetime.fromisoformat(self.target_profit_cache.data[pair]['time_profit_reached']) sell_max, signal_name_max = self.long_bear_exit_profit_target(pair, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) if sell_max and signal_name_max is not None: return True, f"{signal_name_max}_m" if (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_long_bear_stoploss_doom"]): # Update the target, raise it. mark_pair, mark_signal = self.long_bear_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) # Add the pair to the list, if a sell triggered and conditions met if sell and signal_name is not None: previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if ( (previous_profit is None) or (previous_profit < current_profit) ): mark_pair, mark_signal = self.long_bear_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" else: if ( (current_profit >= 0.05) ): previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (previous_profit is None) or (previous_profit < current_profit): mark_signal = "exit_profit_long_bear_max" self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time) if (signal_name not in ["exit_profit_long_bear_max", "exit_long_bear_stoploss_doom", "exit_long_bear_stoploss_u_e"]): if sell and (signal_name is not None): return True, f"{signal_name}" return False, None def long_bear_mark_profit_target(self, pair: str, sell: bool, signal_name: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1) -> tuple: if sell and (signal_name is not None): return pair, signal_name return None, None def long_bear_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) -> tuple: if (previous_sell_reason in ["exit_long_bear_stoploss_doom"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < -0.18): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.1): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (current_profit < -0.04): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason else: if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_long_bear_stoploss_u_e"]): if (current_profit > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (previous_sell_reason in ["exit_profit_long_bear_max"]): if (current_profit < -0.08): # profit is under the threshold, cancel it self._remove_profit_target(pair) return False, None if (0.001 <= current_profit < 0.01): if (current_profit < (previous_profit - 0.01)): return True, previous_sell_reason elif (0.01 <= current_profit < 0.02): if (current_profit < (previous_profit - 0.02)): return True, previous_sell_reason elif (0.02 <= current_profit < 0.03): if (current_profit < (previous_profit - 0.03)): return True, previous_sell_reason elif (0.03 <= current_profit < 0.05): if (current_profit < (previous_profit - 0.04)): return True, previous_sell_reason elif (0.05 <= current_profit < 0.08): if (current_profit < (previous_profit - 0.05)): return True, previous_sell_reason elif (0.08 <= current_profit < 0.12): if (current_profit < (previous_profit - 0.06)): return True, previous_sell_reason elif (0.12 <= current_profit): if (current_profit < (previous_profit - 0.07)): return True, previous_sell_reason else: return False, None return False, None def exit_long_bear_signals(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: # Sell signal 1 if (last_candle['rsi_14'] > 78.0) and (last_candle['rsi_14_4h'] > 50.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']) and (previous_candle_3['close'] > previous_candle_3['bb20_2_upp']) and (previous_candle_4['close'] > previous_candle_4['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_long_bear_1_1_1' else: if (current_profit > 0.01): return True, 'exit_long_bear_1_2_1' # Sell signal 2 elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_4h'] > 50.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_long_bear_2_1_1' else: if (current_profit > 0.01): return True, 'exit_long_bear_2_2_1' # Sell signal 3 elif (last_candle['rsi_14'] > 81.0) and (last_candle['rsi_14_4h'] > 50.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_long_bear_3_1_1' else: if (current_profit > 0.01): return True, 'exit_long_bear_3_2_1' # Sell signal 4 elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_1h'] > 79.0) and (last_candle['rsi_14_4h'] > 50.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_long_bear_4_1_1' else: if (current_profit > 0.01): return True, 'exit_long_bear_4_2_1' # Sell signal 6 elif (last_candle['close'] < last_candle['ema_200']) and (last_candle['close'] > last_candle['ema_50']) and (last_candle['rsi_14'] > 78.5): if (current_profit > 0.01): return True, 'exit_long_bear_6_1' # Sell signal 7 elif (last_candle['rsi_14_1h'] > 79.0) and (last_candle['crossed_below_ema_12_26']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_long_bear_7_1_1' else: if (current_profit > 0.01): return True, 'exit_long_bear_7_2_1' # Sell signal 8 elif (last_candle['close'] > last_candle['bb20_2_upp_1h'] * 1.07) and (last_candle['rsi_14_4h'] > 50.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'exit_long_bear_8_1_1' else: if (current_profit > 0.01): return True, 'exit_long_bear_8_2_1' return False, None def exit_long_bear_main(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if (last_candle['close'] > last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 12.0): return True, 'exit_long_bear_o_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 30.0): return True, 'exit_long_bear_o_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 32.0): return True, 'exit_long_bear_o_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 34.0): return True, 'exit_long_bear_o_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 36.0): return True, 'exit_long_bear_o_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 38.0): return True, 'exit_long_bear_o_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 40.0): return True, 'exit_long_bear_o_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 42.0): return True, 'exit_long_bear_o_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 44.0): return True, 'exit_long_bear_o_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 46.0): return True, 'exit_long_bear_o_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 48.0): return True, 'exit_long_bear_o_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 46.0): return True, 'exit_long_bear_o_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 44.0): return True, 'exit_long_bear_o_12' elif (last_candle['close'] < last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 14.0): return True, 'exit_long_bear_u_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 32.0): return True, 'exit_long_bear_u_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 34.0): return True, 'exit_long_bear_u_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 36.0): return True, 'exit_long_bear_u_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 38.0): return True, 'exit_long_bear_u_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 40.0): return True, 'exit_long_bear_u_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 42.0): return True, 'exit_long_bear_u_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 44.0): return True, 'exit_long_bear_u_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 46.0): return True, 'exit_long_bear_u_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 48.0): return True, 'exit_long_bear_u_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 50.0): return True, 'exit_long_bear_u_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 48.0): return True, 'exit_long_bear_u_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 46.0): return True, 'exit_long_bear_u_12' return False, None def exit_long_bear_r(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if 0.01 > current_profit >= 0.001: if (last_candle['r_480'] > -0.1): return True, 'exit_long_bear_w_0_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bear_w_0_2' elif 0.02 > current_profit >= 0.01: if (last_candle['r_480'] > -0.2): return True, 'exit_long_bear_w_1_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bear_w_1_2' elif 0.03 > current_profit >= 0.02: if (last_candle['r_480'] > -0.3): return True, 'exit_long_bear_w_2_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bear_w_2_2' elif 0.04 > current_profit >= 0.03: if (last_candle['r_480'] > -0.4): return True, 'exit_long_bear_w_3_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bear_w_3_2' elif 0.05 > current_profit >= 0.04: if (last_candle['r_480'] > -0.5): return True, 'exit_long_bear_w_4_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bear_w_4_2' elif 0.06 > current_profit >= 0.05: if (last_candle['r_480'] > -0.6): return True, 'exit_long_bear_w_5_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bear_w_5_2' elif 0.07 > current_profit >= 0.06: if (last_candle['r_480'] > -0.7): return True, 'exit_long_bear_w_6_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bear_w_6_2' elif 0.08 > current_profit >= 0.07: if (last_candle['r_480'] > -0.8): return True, 'exit_long_bear_w_7_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bear_w_7_2' elif 0.09 > current_profit >= 0.08: if (last_candle['r_480'] > -0.9): return True, 'exit_long_bear_w_8_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bear_w_8_2' elif 0.1 > current_profit >= 0.09: if (last_candle['r_480'] > -1.0): return True, 'exit_long_bear_w_9_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bear_w_9_2' elif 0.12 > current_profit >= 0.1: if (last_candle['r_480'] > -1.1): return True, 'exit_long_bear_w_10_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bear_w_10_2' elif 0.2 > current_profit >= 0.12: if (last_candle['r_480'] > -0.4): return True, 'exit_long_bear_w_11_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, 'exit_long_bear_w_11_2' elif current_profit >= 0.2: if (last_candle['r_480'] > -0.2): return True, 'exit_long_bear_w_12_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0): return True, 'exit_long_bear_w_12_2' return False, None def exit_long_bear_stoploss(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: # Stoploss doom if ( (self.stop_thresholds_long[11]) and (current_profit < self.stop_thresholds_long[1]) ): return True, 'exit_long_bear_stoploss_doom' # Under & near EMA200, local uptrend move if ( (self.stop_thresholds_long[13]) and (current_profit < self.stop_thresholds_long[3]) and (last_candle['close'] < last_candle['ema_200']) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_long[7]) and (last_candle['rsi_14'] > previous_candle_1['rsi_14']) and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_long[9])) and (current_time - timedelta(minutes=self.stop_thresholds_long[5]) > trade.open_date_utc) ): return True, 'exit_long_bear_stoploss_u_e' return False, None def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() previous_candle_1 = dataframe.iloc[-2].squeeze() previous_candle_2 = dataframe.iloc[-3].squeeze() previous_candle_3 = dataframe.iloc[-4].squeeze() previous_candle_4 = dataframe.iloc[-5].squeeze() previous_candle_5 = dataframe.iloc[-6].squeeze() enter_tag = 'empty' if hasattr(trade, 'enter_tag') and trade.enter_tag is not None: enter_tag = trade.enter_tag enter_tags = enter_tag.split() profit = current_profit max_profit = ((trade.max_rate - trade.open_rate) / trade.open_rate) max_loss = ((trade.open_rate - trade.min_rate) / trade.min_rate) if hasattr(trade, 'select_filled_orders'): filled_entries = trade.select_filled_orders('enter_long') count_of_entries = len(filled_entries) if count_of_entries > 1: initial_entry = filled_entries[0] if (initial_entry is not None and initial_entry.average is not None): max_profit = ((trade.max_rate - initial_entry.average) / initial_entry.average) max_loss = ((initial_entry.average - trade.min_rate) / trade.min_rate) # Normal mode, bull if any(c in self.normal_mode_bull_tags for c in enter_tags): sell, signal_name = self.exit_normal_bull(pair, current_rate, profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) if sell and (signal_name is not None): return f"{signal_name} ( {enter_tag})" # Normal mode, bear if any(c in self.normal_mode_bear_tags for c in enter_tags): sell, signal_name = self.exit_normal_bear(pair, current_rate, profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) if sell and (signal_name is not None): return f"{signal_name} ( {enter_tag})" # Pump mpde, bull if any(c in self.pump_mode_bull_tags for c in enter_tags): sell, signal_name = self.exit_pump_bull(pair, current_rate, profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) if sell and (signal_name is not None): return f"{signal_name} ( {enter_tag})" # Pump mode, bear if any(c in self.pump_mode_bear_tags for c in enter_tags): sell, signal_name = self.exit_pump_bear(pair, current_rate, profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) if sell and (signal_name is not None): return f"{signal_name} ( {enter_tag})" # Quick mode, bull if any(c in self.quick_mode_bull_tags for c in enter_tags): sell, signal_name = self.exit_quick_bull(pair, current_rate, profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) if sell and (signal_name is not None): return f"{signal_name} ( {enter_tag})" # Quick mode, bear if any(c in self.quick_mode_bear_tags for c in enter_tags): sell, signal_name = self.exit_quick_bear(pair, current_rate, profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) if sell and (signal_name is not None): return f"{signal_name} ( {enter_tag})" # Rebuy mode, bull if all(c in self.rebuy_mode_bull_tags for c in enter_tags): sell, signal_name = self.exit_rebuy_bull(pair, current_rate, profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) if sell and (signal_name is not None): return f"{signal_name} ( {enter_tag})" # Rebuy mode, bear if all(c in self.rebuy_mode_bear_tags for c in enter_tags): sell, signal_name = self.exit_rebuy_bear(pair, current_rate, profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) if sell and (signal_name is not None): return f"{signal_name} ( {enter_tag})" # Long mode, bull if any(c in self.long_mode_bull_tags for c in enter_tags): sell, signal_name = self.exit_long_bull(pair, current_rate, profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) if sell and (signal_name is not None): return f"{signal_name} ( {enter_tag})" # Long mode, bear if any(c in self.long_mode_bear_tags for c in enter_tags): sell, signal_name = self.exit_long_bear(pair, current_rate, profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) if sell and (signal_name is not None): return f"{signal_name} ( {enter_tag})" return None def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: Optional[float], max_stake: float, leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: if (self.position_adjustment_enable == True): enter_tags = entry_tag.split() if all(c in self.rebuy_mode_bull_tags for c in enter_tags): return proposed_stake * self.stake_rebuy_mode_bull_multiplier # Rebuy mode, bear if all(c in self.rebuy_mode_bear_tags for c in enter_tags): return proposed_stake * self.stake_rebuy_mode_bear_multiplier return proposed_stake def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: Optional[float], max_stake: float, current_entry_rate: float, current_exit_rate: float, current_entry_profit: float, current_exit_profit: float, **kwargs) -> Optional[float]: if (self.position_adjustment_enable == False): return None enter_tag = 'empty' if hasattr(trade, 'enter_tag') and trade.enter_tag is not None: enter_tag = trade.enter_tag enter_tags = enter_tag.split() # Rebuy mode, bull if all(c in self.rebuy_mode_bull_tags for c in enter_tags): return self.rebuy_bull_adjust_trade_position(trade, current_time, current_rate, current_profit, min_stake, max_stake, current_entry_rate, current_exit_rate, current_entry_profit, current_exit_profit ) # Rebuy mode, bear if all(c in self.rebuy_mode_bear_tags for c in enter_tags): return self.rebuy_bear_adjust_trade_position(trade, current_time, current_rate, current_profit, min_stake, max_stake, current_entry_rate, current_exit_rate, current_entry_profit, current_exit_profit ) return None def rebuy_bull_adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: Optional[float], max_stake: float, current_entry_rate: float, current_exit_rate: float, current_entry_profit: float, current_exit_profit: float, **kwargs) -> Optional[float]: dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) if(len(dataframe) < 2): return None last_candle = dataframe.iloc[-1].squeeze() previous_candle = dataframe.iloc[-2].squeeze() filled_orders = trade.select_filled_orders() filled_entries = trade.select_filled_orders(trade.entry_side) filled_exits = trade.select_filled_orders(trade.exit_side) count_of_entries = trade.nr_of_successful_entries count_of_exits = trade.nr_of_successful_exits if (count_of_entries == 0): return None is_rebuy = False if (0 < count_of_entries <= self.pa_rebuy_mode_bull_max): if ( (current_profit < self.pa_rebuy_mode_bull_pcts[count_of_entries - 1]) and ( (last_candle['rsi_3'] > 10.0) and (last_candle['rsi_14'] < 40.0) and (last_candle['rsi_3_1h'] > 10.0) and (last_candle['close_max_48'] < (last_candle['close'] * 1.1)) and (last_candle['btc_pct_close_max_72_5m'] < 1.03) ) ): is_rebuy = True if is_rebuy: # This returns first order stake size stake_amount = filled_entries[0].cost print('rebuying..') stake_amount = stake_amount * self.pa_rebuy_mode_bull_multi[count_of_entries - 1] return stake_amount return None def rebuy_bear_adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: Optional[float], max_stake: float, current_entry_rate: float, current_exit_rate: float, current_entry_profit: float, current_exit_profit: float, **kwargs) -> Optional[float]: dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) if(len(dataframe) < 2): return None last_candle = dataframe.iloc[-1].squeeze() previous_candle = dataframe.iloc[-2].squeeze() filled_orders = trade.select_filled_orders() filled_entries = trade.select_filled_orders(trade.entry_side) filled_exits = trade.select_filled_orders(trade.exit_side) count_of_entries = trade.nr_of_successful_entries count_of_exits = trade.nr_of_successful_exits if (count_of_entries == 0): return None is_rebuy = False if (0 < count_of_entries <= self.pa_rebuy_mode_bear_max): if ( (current_profit < self.pa_rebuy_mode_bear_pcts[count_of_entries - 1]) and ( (last_candle['rsi_3'] > 10.0) and (last_candle['rsi_14'] < 40.0) and (last_candle['rsi_3_1h'] > 10.0) and (last_candle['close_max_48'] < (last_candle['close'] * 1.1)) and (last_candle['btc_pct_close_max_72_5m'] < 1.03) ) ): is_rebuy = True if is_rebuy: # This returns first order stake size stake_amount = filled_entries[0].cost print('rebuying..') stake_amount = stake_amount * self.pa_rebuy_mode_bear_multi[count_of_entries - 1] return stake_amount return None def informative_pairs(self): # get access to all pairs available in whitelist. pairs = self.dp.current_whitelist() # Assign tf to each pair so they can be downloaded and cached for strategy. informative_pairs = [] for info_timeframe in self.info_timeframes: informative_pairs.extend([(pair, info_timeframe) for pair in pairs]) if self.config['stake_currency'] in ['USDT','BUSD','USDC','DAI','TUSD','PAX','USD','EUR','GBP']: btc_info_pair = f"BTC/{self.config['stake_currency']}" else: btc_info_pair = "BTC/USDT" informative_pairs.extend([(btc_info_pair, btc_info_timeframe) for btc_info_timeframe in self.btc_info_timeframes]) return informative_pairs def informative_1d_indicators(self, metadata: dict, info_timeframe) -> DataFrame: tik = time.perf_counter() assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1d = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=info_timeframe) # Indicators # ----------------------------------------------------------------------------------------- # RSI informative_1d['rsi_14'] = ta.RSI(informative_1d, timeperiod=14) # CTI informative_1d['cti_20'] = pta.cti(informative_1d["close"], length=20) # Pivots informative_1d['pivot'], informative_1d['res1'], informative_1d['res2'], informative_1d['res3'], informative_1d['sup1'], informative_1d['sup2'], informative_1d['sup3'] = pivot_points(informative_1d, mode='fibonacci') # S/R res_series = informative_1d['high'].rolling(window = 5, center=True).apply(lambda row: is_resistance(row), raw=True).shift(2) sup_series = informative_1d['low'].rolling(window = 5, center=True).apply(lambda row: is_support(row), raw=True).shift(2) informative_1d['res_level'] = Series(np.where(res_series, np.where(informative_1d['close'] > informative_1d['open'], informative_1d['close'], informative_1d['open']), float('NaN'))).ffill() informative_1d['res_hlevel'] = Series(np.where(res_series, informative_1d['high'], float('NaN'))).ffill() informative_1d['sup_level'] = Series(np.where(sup_series, np.where(informative_1d['close'] < informative_1d['open'], informative_1d['close'], informative_1d['open']), float('NaN'))).ffill() # Downtrend checks informative_1d['is_downtrend_3'] = ((informative_1d['close'] < informative_1d['open']) & (informative_1d['close'].shift(1) < informative_1d['open'].shift(1)) & (informative_1d['close'].shift(2) < informative_1d['open'].shift(2))) informative_1d['is_downtrend_5'] = ((informative_1d['close'] < informative_1d['open']) & (informative_1d['close'].shift(1) < informative_1d['open'].shift(1)) & (informative_1d['close'].shift(2) < informative_1d['open'].shift(2)) & (informative_1d['close'].shift(3) < informative_1d['open'].shift(3)) & (informative_1d['close'].shift(4) < informative_1d['open'].shift(4))) # Wicks informative_1d['top_wick_pct'] = ((informative_1d['high'] - np.maximum(informative_1d['open'], informative_1d['close'])) / np.maximum(informative_1d['open'], informative_1d['close'])) # Candle change informative_1d['change_pct'] = (informative_1d['close'] - informative_1d['open']) / informative_1d['open'] # Performance logging # ----------------------------------------------------------------------------------------- tok = time.perf_counter() log.debug(f"[{metadata['pair']}] informative_1d_indicators took: {tok - tik:0.4f} seconds.") return informative_1d def informative_4h_indicators(self, metadata: dict, info_timeframe) -> DataFrame: tik = time.perf_counter() assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_4h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=info_timeframe) # Indicators # ----------------------------------------------------------------------------------------- # RSI informative_4h['rsi_14'] = ta.RSI(informative_4h, timeperiod=14, fillna=True) informative_4h['rsi_14_max_6'] = informative_4h['rsi_14'].rolling(6).max() # EMA informative_4h['ema_12'] = ta.EMA(informative_4h, timeperiod=12) informative_4h['ema_26'] = ta.EMA(informative_4h, timeperiod=26) informative_4h['ema_50'] = ta.EMA(informative_4h, timeperiod=50) informative_4h['ema_100'] = ta.EMA(informative_4h, timeperiod=100) informative_4h['ema_200'] = ta.EMA(informative_4h, timeperiod=200) # SMA informative_4h['sma_12'] = ta.SMA(informative_4h, timeperiod=12) informative_4h['sma_26'] = ta.SMA(informative_4h, timeperiod=26) informative_4h['sma_50'] = ta.SMA(informative_4h, timeperiod=50) informative_4h['sma_200'] = ta.SMA(informative_4h, timeperiod=200) # Williams %R informative_4h['r_14'] = williams_r(informative_4h, period=14) informative_4h['r_480'] = williams_r(informative_4h, period=480) # CTI informative_4h['cti_20'] = pta.cti(informative_4h["close"], length=20) # S/R res_series = informative_4h['high'].rolling(window = 5, center=True).apply(lambda row: is_resistance(row), raw=True).shift(2) sup_series = informative_4h['low'].rolling(window = 5, center=True).apply(lambda row: is_support(row), raw=True).shift(2) informative_4h['res_level'] = Series(np.where(res_series, np.where(informative_4h['close'] > informative_4h['open'], informative_4h['close'], informative_4h['open']), float('NaN'))).ffill() informative_4h['res_hlevel'] = Series(np.where(res_series, informative_4h['high'], float('NaN'))).ffill() informative_4h['sup_level'] = Series(np.where(sup_series, np.where(informative_4h['close'] < informative_4h['open'], informative_4h['close'], informative_4h['open']), float('NaN'))).ffill() # Downtrend checks informative_4h['not_downtrend'] = ((informative_4h['close'] > informative_4h['close'].shift(2)) | (informative_4h['rsi_14'] > 50.0)) informative_4h['is_downtrend_3'] = ((informative_4h['close'] < informative_4h['open']) & (informative_4h['close'].shift(1) < informative_4h['open'].shift(1)) & (informative_4h['close'].shift(2) < informative_4h['open'].shift(2))) # Wicks informative_4h['top_wick_pct'] = ((informative_4h['high'] - np.maximum(informative_4h['open'], informative_4h['close'])) / np.maximum(informative_4h['open'], informative_4h['close'])) # Candle change informative_4h['change_pct'] = (informative_4h['close'] - informative_4h['open']) / informative_4h['open'] # Max highs informative_4h['high_max_3'] = informative_4h['high'].rolling(3).max() informative_4h['high_max_12'] = informative_4h['high'].rolling(12).max() informative_4h['high_max_24'] = informative_4h['high'].rolling(24).max() informative_4h['high_max_36'] = informative_4h['high'].rolling(36).max() informative_4h['high_max_48'] = informative_4h['high'].rolling(48).max() informative_4h['pct_change_high_max_1_12'] = (informative_4h['high'] - informative_4h['high_max_12']) / informative_4h['high_max_12'] informative_4h['pct_change_high_max_3_12'] = (informative_4h['high_max_3'] - informative_4h['high_max_12']) / informative_4h['high_max_12'] informative_4h['pct_change_high_max_3_24'] = (informative_4h['high_max_3'] - informative_4h['high_max_24']) / informative_4h['high_max_24'] informative_4h['pct_change_high_max_3_36'] = (informative_4h['high_max_3'] - informative_4h['high_max_36']) / informative_4h['high_max_36'] informative_4h['pct_change_high_max_3_48'] = (informative_4h['high_max_3'] - informative_4h['high_max_48']) / informative_4h['high_max_48'] # Volume informative_4h['volume_mean_factor_6'] = informative_4h['volume'] / informative_4h['volume'].rolling(6).mean() # Performance logging # ----------------------------------------------------------------------------------------- tok = time.perf_counter() log.debug(f"[{metadata['pair']}] informative_1d_indicators took: {tok - tik:0.4f} seconds.") return informative_4h def informative_1h_indicators(self, metadata: dict, info_timeframe) -> DataFrame: tik = time.perf_counter() assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=info_timeframe) # Indicators # ----------------------------------------------------------------------------------------- # RSI informative_1h['rsi_3'] = ta.RSI(informative_1h, timeperiod=3) informative_1h['rsi_14'] = ta.RSI(informative_1h, timeperiod=14) # EMA informative_1h['ema_12'] = ta.EMA(informative_1h, timeperiod=12) informative_1h['ema_26'] = ta.EMA(informative_1h, timeperiod=26) informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) informative_1h['ema_100'] = ta.EMA(informative_1h, timeperiod=100) informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # SMA informative_1h['sma_12'] = ta.SMA(informative_1h, timeperiod=12) informative_1h['sma_26'] = ta.SMA(informative_1h, timeperiod=26) informative_1h['sma_50'] = ta.SMA(informative_1h, timeperiod=50) informative_1h['sma_100'] = ta.SMA(informative_1h, timeperiod=100) informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200) # BB bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2) informative_1h['bb20_2_low'] = bollinger['lower'] informative_1h['bb20_2_mid'] = bollinger['mid'] informative_1h['bb20_2_upp'] = bollinger['upper'] informative_1h['bb20_2_width'] = ((informative_1h['bb20_2_upp'] - informative_1h['bb20_2_low']) / informative_1h['bb20_2_mid']) # Williams %R informative_1h['r_14'] = williams_r(informative_1h, period=14) informative_1h['r_96'] = williams_r(informative_1h, period=96) informative_1h['r_480'] = williams_r(informative_1h, period=480) # CTI informative_1h['cti_20'] = pta.cti(informative_1h["close"], length=20) informative_1h['cti_40'] = pta.cti(informative_1h["close"], length=40) # S/R res_series = informative_1h['high'].rolling(window = 5, center=True).apply(lambda row: is_resistance(row), raw=True).shift(2) sup_series = informative_1h['low'].rolling(window = 5, center=True).apply(lambda row: is_support(row), raw=True).shift(2) informative_1h['res_level'] = Series(np.where(res_series, np.where(informative_1h['close'] > informative_1h['open'], informative_1h['close'], informative_1h['open']), float('NaN'))).ffill() informative_1h['res_hlevel'] = Series(np.where(res_series, informative_1h['high'], float('NaN'))).ffill() informative_1h['sup_level'] = Series(np.where(sup_series, np.where(informative_1h['close'] < informative_1h['open'], informative_1h['close'], informative_1h['open']), float('NaN'))).ffill() # Pump protections informative_1h['hl_pct_change_48'] = range_percent_change(self, informative_1h, 'HL', 48) informative_1h['hl_pct_change_36'] = range_percent_change(self, informative_1h, 'HL', 36) informative_1h['hl_pct_change_24'] = range_percent_change(self, informative_1h, 'HL', 24) informative_1h['hl_pct_change_12'] = range_percent_change(self, informative_1h, 'HL', 12) informative_1h['hl_pct_change_6'] = range_percent_change(self, informative_1h, 'HL', 6) # Downtrend checks informative_1h['not_downtrend'] = ((informative_1h['close'] > informative_1h['close'].shift(2)) | (informative_1h['rsi_14'] > 50.0)) informative_1h['is_downtrend_3'] = ((informative_1h['close'] < informative_1h['open']) & (informative_1h['close'].shift(1) < informative_1h['open'].shift(1)) & (informative_1h['close'].shift(2) < informative_1h['open'].shift(2))) informative_1h['is_downtrend_5'] = ((informative_1h['close'] < informative_1h['open']) & (informative_1h['close'].shift(1) < informative_1h['open'].shift(1)) & (informative_1h['close'].shift(2) < informative_1h['open'].shift(2)) & (informative_1h['close'].shift(3) < informative_1h['open'].shift(3)) & (informative_1h['close'].shift(4) < informative_1h['open'].shift(4))) # Wicks informative_1h['top_wick_pct'] = ((informative_1h['high'] - np.maximum(informative_1h['open'], informative_1h['close'])) / np.maximum(informative_1h['open'], informative_1h['close'])) # Candle change informative_1h['change_pct'] = (informative_1h['close'] - informative_1h['open']) / informative_1h['open'] # Max highs informative_1h['high_max_3'] = informative_1h['high'].rolling(3).max() informative_1h['high_max_6'] = informative_1h['high'].rolling(6).max() informative_1h['high_max_12'] = informative_1h['high'].rolling(12).max() informative_1h['high_max_24'] = informative_1h['high'].rolling(24).max() informative_1h['high_max_36'] = informative_1h['high'].rolling(36).max() informative_1h['high_max_48'] = informative_1h['high'].rolling(48).max() informative_1h['pct_change_high_max_3_12'] = (informative_1h['high_max_3'] - informative_1h['high_max_12']) / informative_1h['high_max_12'] informative_1h['pct_change_high_max_6_12'] = (informative_1h['high_max_6'] - informative_1h['high_max_12']) / informative_1h['high_max_12'] informative_1h['pct_change_high_max_6_24'] = (informative_1h['high_max_6'] - informative_1h['high_max_24']) / informative_1h['high_max_24'] # Volume informative_1h['volume_mean_factor_12'] = informative_1h['volume'] / informative_1h['volume'].rolling(12).mean() # Performance logging # ----------------------------------------------------------------------------------------- tok = time.perf_counter() log.debug(f"[{metadata['pair']}] informative_1h_indicators took: {tok - tik:0.4f} seconds.") return informative_1h def informative_15m_indicators(self, metadata: dict, info_timeframe) -> DataFrame: tik = time.perf_counter() assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=info_timeframe) # Indicators # ----------------------------------------------------------------------------------------- # RSI informative_15m['rsi_3'] = ta.RSI(informative_15m, timeperiod=3) informative_15m['rsi_14'] = ta.RSI(informative_15m, timeperiod=14) # SMA informative_15m['sma_200'] = ta.SMA(informative_15m, timeperiod=200) # CTI informative_15m['cti_20'] = pta.cti(informative_15m["close"], length=20) # Downtrend check informative_15m['not_downtrend'] = ((informative_15m['close'] > informative_15m['open']) | (informative_15m['close'].shift(1) > informative_15m['open'].shift(1)) | (informative_15m['close'].shift(2) > informative_15m['open'].shift(2)) | (informative_15m['rsi_14'] > 50.0) | (informative_15m['rsi_3'] > 25.0)) # Volume informative_15m['volume_mean_factor_12'] = informative_15m['volume'] / informative_15m['volume'].rolling(12).mean() # Performance logging # ----------------------------------------------------------------------------------------- tok = time.perf_counter() log.debug(f"[{metadata['pair']}] informative_15m_indicators took: {tok - tik:0.4f} seconds.") return informative_15m # Coin Pair Base Timeframe Indicators # --------------------------------------------------------------------------------------------- def base_tf_5m_indicators(self, metadata: dict, dataframe: DataFrame) -> DataFrame: tik = time.perf_counter() # Indicators # ----------------------------------------------------------------------------------------- # RSI dataframe['rsi_3'] = ta.RSI(dataframe, timeperiod=3) dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) # EMA dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) dataframe['ema_200_pct_change_144'] = ((dataframe['ema_200'] - dataframe['ema_200'].shift(144)) / dataframe['ema_200'].shift(144)) dataframe['ema_200_pct_change_288'] = ((dataframe['ema_200'] - dataframe['ema_200'].shift(288)) / dataframe['ema_200'].shift(288)) # SMA dataframe['sma_50'] = ta.SMA(dataframe, timeperiod=50) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) # BB 20 - STD2 bb_20_std2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb20_2_low'] = bb_20_std2['lower'] dataframe['bb20_2_mid'] = bb_20_std2['mid'] dataframe['bb20_2_upp'] = bb_20_std2['upper'] # BB 40 - STD2 bb_40_std2 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2) dataframe['bb40_2_low'] = bb_40_std2['lower'] dataframe['bb40_2_mid'] = bb_40_std2['mid'] dataframe['bb40_2_delta'] = (bb_40_std2['mid'] - dataframe['bb40_2_low']).abs() dataframe['bb40_2_tail'] = (dataframe['close'] - dataframe['bb40_2_low']).abs() # Williams %R dataframe['r_14'] = williams_r(dataframe, period=14) dataframe['r_480'] = williams_r(dataframe, period=480) # CTI dataframe['cti_20'] = pta.cti(dataframe["close"], length=20) # Heiken Ashi heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] # Dip protection dataframe['tpct_change_0'] = top_percent_change(self, dataframe, 0) dataframe['tpct_change_2'] = top_percent_change(self, dataframe, 2) # Close max dataframe['close_max_12'] = dataframe['close'].rolling(12).max() dataframe['close_max_24'] = dataframe['close'].rolling(24).max() dataframe['close_max_48'] = dataframe['close'].rolling(48).max() dataframe['pct_close_max_48'] = (dataframe['close_max_48'] - dataframe['close']) / dataframe['close'] # Close delta dataframe['close_delta'] = (dataframe['close'] - dataframe['close'].shift()).abs() # For sell checks dataframe['crossed_below_ema_12_26'] = qtpylib.crossed_below(dataframe['ema_12'], dataframe['ema_26']) # Global protections # ----------------------------------------------------------------------------------------- if not self.config['runmode'].value in ('live', 'dry_run'): # Backtest age filter dataframe['bt_agefilter_ok'] = False dataframe.loc[dataframe.index > (12 * 24 * self.bt_min_age_days),'bt_agefilter_ok'] = True else: # Exchange downtime protection dataframe['live_data_ok'] = (dataframe['volume'].rolling(window=72, min_periods=72).min() > 0) # Performance logging # ----------------------------------------------------------------------------------------- tok = time.perf_counter() log.debug(f"[{metadata['pair']}] base_tf_5m_indicators took: {tok - tik:0.4f} seconds.") return dataframe # Coin Pair Indicator Switch Case # --------------------------------------------------------------------------------------------- def info_switcher(self, metadata: dict, info_timeframe) -> DataFrame: if info_timeframe == '1d': return self.informative_1d_indicators(metadata, info_timeframe) elif info_timeframe == '4h': return self.informative_4h_indicators(metadata, info_timeframe) elif info_timeframe == '1h': return self.informative_1h_indicators(metadata, info_timeframe) elif info_timeframe == '15m': return self.informative_15m_indicators(metadata, info_timeframe) else: raise RuntimeError(f"{info_timeframe} not supported as informative timeframe for BTC pair.") # BTC 1D Indicators # --------------------------------------------------------------------------------------------- def btc_info_1d_indicators(self, btc_info_pair, btc_info_timeframe, metadata: dict) -> DataFrame: tik = time.perf_counter() btc_info_1d = self.dp.get_pair_dataframe(btc_info_pair, btc_info_timeframe) # Indicators # ----------------------------------------------------------------------------------------- btc_info_1d['rsi_14'] = ta.RSI(btc_info_1d, timeperiod=14) #btc_info_1d['pivot'], btc_info_1d['res1'], btc_info_1d['res2'], btc_info_1d['res3'], btc_info_1d['sup1'], btc_info_1d['sup2'], btc_info_1d['sup3'] = pivot_points(btc_info_1d, mode='fibonacci') # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ['date'] btc_info_1d.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] btc_info_1d_indicators took: {tok - tik:0.4f} seconds.") return btc_info_1d # BTC 4h Indicators # --------------------------------------------------------------------------------------------- def btc_info_4h_indicators(self, btc_info_pair, btc_info_timeframe, metadata: dict) -> DataFrame: tik = time.perf_counter() btc_info_4h = self.dp.get_pair_dataframe(btc_info_pair, btc_info_timeframe) # Indicators # ----------------------------------------------------------------------------------------- # RSI btc_info_4h['rsi_14'] = ta.RSI(btc_info_4h, timeperiod=14) # SMA btc_info_4h['sma_200'] = ta.SMA(btc_info_4h, timeperiod=200) # Bull market or not btc_info_4h['is_bull'] = btc_info_4h['close'] > btc_info_4h['sma_200'] # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ['date'] btc_info_4h.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] btc_info_4h_indicators took: {tok - tik:0.4f} seconds.") return btc_info_4h # BTC 1h Indicators # --------------------------------------------------------------------------------------------- def btc_info_1h_indicators(self, btc_info_pair, btc_info_timeframe, metadata: dict) -> DataFrame: tik = time.perf_counter() btc_info_1h = self.dp.get_pair_dataframe(btc_info_pair, btc_info_timeframe) # Indicators # ----------------------------------------------------------------------------------------- # RSI btc_info_1h['rsi_14'] = ta.RSI(btc_info_1h, timeperiod=14) btc_info_1h['not_downtrend'] = ((btc_info_1h['close'] > btc_info_1h['close'].shift(2)) | (btc_info_1h['rsi_14'] > 50)) # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ['date'] btc_info_1h.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] btc_info_1h_indicators took: {tok - tik:0.4f} seconds.") return btc_info_1h # BTC 15m Indicators # --------------------------------------------------------------------------------------------- def btc_info_15m_indicators(self, btc_info_pair, btc_info_timeframe, metadata: dict) -> DataFrame: tik = time.perf_counter() btc_info_15m = self.dp.get_pair_dataframe(btc_info_pair, btc_info_timeframe) # Indicators # ----------------------------------------------------------------------------------------- btc_info_15m['rsi_14'] = ta.RSI(btc_info_15m, timeperiod=14) # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ['date'] btc_info_15m.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] btc_info_15m_indicators took: {tok - tik:0.4f} seconds.") return btc_info_15m # BTC 5m Indicators # --------------------------------------------------------------------------------------------- def btc_info_5m_indicators(self, btc_info_pair, btc_info_timeframe, metadata: dict) -> DataFrame: tik = time.perf_counter() btc_info_5m = self.dp.get_pair_dataframe(btc_info_pair, btc_info_timeframe) # Indicators # ----------------------------------------------------------------------------------------- # RSI btc_info_5m['rsi_14'] = ta.RSI(btc_info_5m, timeperiod=14) # Close max btc_info_5m['close_max_24'] = btc_info_5m['close'].rolling(24).max() btc_info_5m['close_max_72'] = btc_info_5m['close'].rolling(72).max() btc_info_5m['pct_close_max_24'] = (btc_info_5m['close_max_24'] - btc_info_5m['close']) / btc_info_5m['close'] btc_info_5m['pct_close_max_72'] = (btc_info_5m['close_max_72'] - btc_info_5m['close']) / btc_info_5m['close'] # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ['date'] btc_info_5m.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] btc_info_5m_indicators took: {tok - tik:0.4f} seconds.") return btc_info_5m # BTC Indicator Switch Case # --------------------------------------------------------------------------------------------- def btc_info_switcher(self, btc_info_pair, btc_info_timeframe, metadata: dict) -> DataFrame: if btc_info_timeframe == '1d': return self.btc_info_1d_indicators(btc_info_pair, btc_info_timeframe, metadata) elif btc_info_timeframe == '4h': return self.btc_info_4h_indicators(btc_info_pair, btc_info_timeframe, metadata) elif btc_info_timeframe == '1h': return self.btc_info_1h_indicators(btc_info_pair, btc_info_timeframe, metadata) elif btc_info_timeframe == '15m': return self.btc_info_15m_indicators(btc_info_pair, btc_info_timeframe, metadata) elif btc_info_timeframe == '5m': return self.btc_info_5m_indicators(btc_info_pair, btc_info_timeframe, metadata) else: raise RuntimeError(f"{btc_info_timeframe} not supported as informative timeframe for BTC pair.") def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: tik = time.perf_counter() ''' --> BTC informative indicators ___________________________________________________________________________________________ ''' if self.config['stake_currency'] in ['USDT','BUSD','USDC','DAI','TUSD','PAX','USD','EUR','GBP']: btc_info_pair = f"BTC/{self.config['stake_currency']}" else: btc_info_pair = "BTC/USDT" for btc_info_timeframe in self.btc_info_timeframes: btc_informative = self.btc_info_switcher(btc_info_pair, btc_info_timeframe, metadata) dataframe = merge_informative_pair(dataframe, btc_informative, self.timeframe, btc_info_timeframe, ffill=True) # Customize what we drop - in case we need to maintain some BTC informative ohlcv data # Default drop all drop_columns = { '1d': [f"btc_{s}_{btc_info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']], '4h': [f"btc_{s}_{btc_info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']], '1h': [f"btc_{s}_{btc_info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']], '15m': [f"btc_{s}_{btc_info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']], '5m': [f"btc_{s}_{btc_info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']], }.get(btc_info_timeframe,[f"{s}_{btc_info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']]) drop_columns.append(f"date_{btc_info_timeframe}") dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) ''' --> Indicators on informative timeframes ___________________________________________________________________________________________ ''' for info_timeframe in self.info_timeframes: info_indicators = self.info_switcher(metadata, info_timeframe) dataframe = merge_informative_pair(dataframe, info_indicators, self.timeframe, info_timeframe, ffill=True) # Customize what we drop - in case we need to maintain some informative timeframe ohlcv data # Default drop all except base timeframe ohlcv data drop_columns = { '1d': [f"{s}_{info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']], '4h': [f"{s}_{info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']], '1h': [f"{s}_{info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']], '15m': [f"{s}_{info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']] }.get(info_timeframe,[f"{s}_{info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']]) dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) ''' --> The indicators for the base timeframe (5m) ___________________________________________________________________________________________ ''' dataframe = self.base_tf_5m_indicators(metadata, dataframe) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] Populate indicators took a total of: {tok - tik:0.4f} seconds.") return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'enter_tag'] = '' # the number of free slots current_free_slots = self.config["max_open_trades"] - len(LocalTrade.get_trades_proxy(is_open=True)) for buy_enable in self.buy_params: index = int(buy_enable.split('_')[2]) item_buy_protection_list = [True] if self.buy_params[f'{buy_enable}']: # Buy conditions # ----------------------------------------------------------------------------------------- item_buy_logic = [] item_buy_logic.append(reduce(lambda x, y: x & y, item_buy_protection_list)) # Condition #1 - Long mode bull. Uptrend. if index == 1: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h']) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.16)) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.36)) item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.4)) item_buy_logic.append(dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5)) item_buy_logic.append(dataframe['hl_pct_change_24_1h'] < 0.5) item_buy_logic.append(dataframe['hl_pct_change_48_1h'] < 0.75) item_buy_logic.append(dataframe['ema_50_1h'] > dataframe['ema_200_1h']) item_buy_logic.append(dataframe['cti_20_1h'] < 0.95) item_buy_logic.append(dataframe['rsi_14_1d'] < 80.0) item_buy_logic.append(dataframe['r_14_1h'] < -25.0) item_buy_logic.append(dataframe['r_14_4h'] < -25.0) # curent 4h long red, overbought 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.08) | (dataframe['cti_20_4h'] < 0.85)) # current 1d red with top wick, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] > -0.04) | (dataframe['top_wick_pct_1d'] < 0.04) | (dataframe['rsi_14_1d'] < 70.0)) # current 1d long green, current 4h red with top wick, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] < 0.2) | (dataframe['change_pct_4h'] > -0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['cti_20_1d'] < 0.8)) # current 4h red with top wick, drop in last 4 hours item_buy_logic.append((dataframe['change_pct_4h'] > -0.08) | (dataframe['top_wick_pct_4h'] < 0.08) | (dataframe['close_max_48'] < (dataframe['close'] * 1.16))) # current 4h long red, drop in the last 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.1) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close_max_48'] < (dataframe['close'] * 1.12))) # current 4h green with top wick, current 1d red, overbought 1d item_buy_logic.append((dataframe['change_pct_4h'] < 0.04) | (dataframe['top_wick_pct_4h'] < 0.08) | (dataframe['change_pct_1d'] > -0.04) | (dataframe['cti_20_1d'] < 0.85)) # current 4h green with top wick, previous high is higher than current high item_buy_logic.append((dataframe['change_pct_4h'] < 0.01) | (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 3.0)) | (dataframe['pct_change_high_max_3_24_4h'] > -0.1)) # current 4h with relative long top wick, drop in the last 4h item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0)) | (dataframe['close_max_48'] < (dataframe['close'] * 1.1))) #current 4h red, current higher lower than previous high, downtrend 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['pct_change_high_max_3_24_4h'] > -0.12) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 4h red, overbought 4h, downtrend 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 4h long red item_buy_logic.append((dataframe['change_pct_4h'] > -0.12) | (dataframe['close_max_48'] < (dataframe['close'] * 1.16)) | (dataframe['cti_20_1h'] < -0.5)) # current 1d long green with top long wick item_buy_logic.append((dataframe['change_pct_1d'] < 0.12) | (dataframe['top_wick_pct_1d'] < 0.12)) # current 1d long red, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] > -0.12) | (dataframe['cti_20_1d'] < 0.85)) # current 1d red with top wick, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] > -0.06) | (dataframe['top_wick_pct_1d'] < 0.06) | (dataframe['cti_20_1d'] < 0.9)) # current 1d long red, overbought 1d, drop in the last 4h item_buy_logic.append((dataframe['change_pct_1d'] > -0.04) | (dataframe['cti_20_1d'] < 0.9) | (dataframe['close_max_48'] < (dataframe['close'] * 1.12))) # current 1d red, previous 1d red with top wick item_buy_logic.append((dataframe['change_pct_1d'] > -0.04) | (dataframe['change_pct_1d'].shift(288) > -0.04) | (dataframe['top_wick_pct_1d'].shift(288) < 0.04) | (dataframe['cti_20_1d'] < 0.5)) # current 1d red, previous 1d red with top wick item_buy_logic.append((dataframe['change_pct_1d'] > -0.02) | (dataframe['change_pct_1d'].shift(288) > -0.02) | (dataframe['top_wick_pct_1d'].shift(288) < 0.02) | (dataframe['rsi_14_1d'] < 70.0)) # current 1d green with top wick item_buy_logic.append((dataframe['change_pct_1d'] < 0.04) | (dataframe['top_wick_pct_1d'] < 0.04) | (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['is_downtrend_3_1h'] == False)) # current 1d red, previous 1d red, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] > -0.0) | (dataframe['change_pct_1d'].shift(288) > -0.0) | (dataframe['cti_20_1d'] < 0.9)) # current 1d green, overbought 4h item_buy_logic.append((dataframe['change_pct_1d'] < 0.12) | (dataframe['cti_20_4h'] < 0.8)) # drop while near there was overbought 4h item_buy_logic.append((dataframe['close_max_48'] < (dataframe['close'] * 1.12)) | (dataframe['rsi_14_max_6_4h'] < 80.0) | (dataframe['cti_20_4h'] < 0.5)) # current 4h downtrend, drop in last 24h item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.2))) # current 4h downtrend, overbought 4h item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_4h'] < 0.85)) item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['change_pct_1d'] < 0.12)) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0)) # current 1d long top wick item_buy_logic.append(dataframe['top_wick_pct_1d'] < (abs(dataframe['change_pct_1d']) * 10.0)) item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False) | (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_4h'] < 0.5)) item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False) | (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1d'] < 0.75)) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.016)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * 0.999)) # Condition #2 - Normal mode bull. if index == 2: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h']) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append((dataframe['tpct_change_0'] < 0.034)) item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.26)) item_buy_logic.append(dataframe['high_max_6_1h'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.36)) item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.4)) item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)) item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(48)) item_buy_logic.append(dataframe['cti_20_1h'] < 0.5) item_buy_logic.append(dataframe['cti_20_4h'] < 0.95) item_buy_logic.append(dataframe['rsi_14_1d'] < 80.0) item_buy_logic.append(dataframe['r_14_4h'] < -25.0) item_buy_logic.append(dataframe['not_downtrend_15m']) # current 4h green with top wick item_buy_logic.append((dataframe['change_pct_4h'] < 0.04) | (dataframe['top_wick_pct_4h'] < 0.04)) # current 4h red, overbought, 4h downtrend item_buy_logic.append((dataframe['change_pct_4h'] > -0.06) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 4h long red item_buy_logic.append((dataframe['change_pct_4h'] > -0.1) | (dataframe['cti_20_4h'] < 0.85)) # current 4h red with top wick item_buy_logic.append((dataframe['change_pct_4h'] > -0.02) | (dataframe['top_wick_pct_4h'] < 0.02) | (dataframe['cti_20_4h'] < 0.85)) # 3 4h red item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_4h'] < 0.5)) # current and previous 4h red item_buy_logic.append((dataframe['change_pct_4h'] > -0.0) | (dataframe['change_pct_4h'].shift(48) > -0.0) | (dataframe['rsi_14_4h'] < 50.0) | (dataframe['cti_20_4h'] < 0.5)) item_buy_logic.append((dataframe['hl_pct_change_48_1h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # currend 1d red with top wick item_buy_logic.append((dataframe['change_pct_1d'] > -0.12) | (dataframe['top_wick_pct_1d'] < 0.12)) # currend 1d green with top wick item_buy_logic.append((dataframe['change_pct_1d'] < 0.12) | (dataframe['top_wick_pct_1d'] < 0.12)) # currend 1d red with top wick item_buy_logic.append((dataframe['change_pct_1d'] > -0.06) | (dataframe['top_wick_pct_1d'] < 0.06) | (dataframe['rsi_14_1d'] < 70.0)) # current 1d green with top wick, current 4h red item_buy_logic.append((dataframe['change_pct_1d'] < 0.06) | (dataframe['top_wick_pct_1d'] < 0.06) | (dataframe['change_pct_4h'] > -0.06) | (dataframe['cti_20_4h'] < 0.8)) # current 1d red item_buy_logic.append((dataframe['change_pct_1d'] > -0.05) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['cti_20_1d'] < 0.85)) # current 1d red item_buy_logic.append((dataframe['change_pct_1d'] > -0.12) | (dataframe['cti_20_1d'] < 0.8)) # Logic item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.04)) item_buy_logic.append(dataframe['close_delta'].gt(dataframe['close'] * 0.02)) item_buy_logic.append(dataframe['bb40_2_tail'].lt(dataframe['bb40_2_delta'] * 0.2)) item_buy_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift())) item_buy_logic.append(dataframe['close'].le(dataframe['close'].shift())) # Condition #3 - Normal mode bull. if index == 3: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h']) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.26)) item_buy_logic.append(dataframe['ema_12_1h'] > dataframe['ema_200_1h']) item_buy_logic.append(dataframe['ema_12_4h'] > dataframe['ema_200_4h']) item_buy_logic.append(dataframe['rsi_14_4h'] < 75.0) item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0) item_buy_logic.append(dataframe['not_downtrend_15m']) item_buy_logic.append(dataframe['not_downtrend_1h']) item_buy_logic.append(dataframe['not_downtrend_4h']) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) # current 4h green with wick, overbought 4h item_buy_logic.append((dataframe['change_pct_4h'] < 0.1) | (dataframe['top_wick_pct_4h'] < 0.16) | (dataframe['rsi_14_4h'] < 70.0)) # current 4h long green, overbought 4h item_buy_logic.append((dataframe['change_pct_4h'] < 0.12) | (dataframe['rsi_14_4h'] < 70.0)) # current 4h red with top wick item_buy_logic.append((dataframe['change_pct_4h'] > 0.0) | (dataframe['top_wick_pct_4h'] < 0.1) | (dataframe['ema_12_4h'] > dataframe['ema_200_4h'])) # current 4h long red item_buy_logic.append((dataframe['change_pct_4h'] > -0.1) | (dataframe['rsi_14_max_6_4h'] < 85.0)) # current 4h very long top wick item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0)) | (dataframe['rsi_14_max_6_4h'] < 80.0)) # current 4h red with top wick item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['top_wick_pct_4h'] < 0.06) | (dataframe['rsi_14_max_6_4h'] < 80.0)) # current 4h green with top wick item_buy_logic.append((dataframe['change_pct_4h'] < 0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['rsi_14_4h'] < 70.00)) # current 1d long red, previous 1d long green with long top wick item_buy_logic.append((dataframe['change_pct_1d'] > -0.2) | (dataframe['change_pct_1d'].shift(288) < 0.2) | (dataframe['top_wick_pct_1d'].shift(288) < 0.2)) # current 1d green, overbought 4h item_buy_logic.append((dataframe['change_pct_1d'] < 0.12) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_4h'] < 0.8)) # current 1d long green with long green wick item_buy_logic.append((dataframe['change_pct_1d'] < 0.2) | (dataframe['top_wick_pct_1d'] < 0.2)) # current 1d long green, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] < 0.12) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['cti_20_1d'] < 0.8)) # Logic item_buy_logic.append(dataframe['rsi_14'] < 36.0) item_buy_logic.append(dataframe['ha_close'] > dataframe['ha_open']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.018)) # Condition #4 - Normal mode bull. if index == 4: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h']) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.36)) item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.4)) item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5)) item_buy_logic.append(dataframe['cti_20_1h'] < 0.9) item_buy_logic.append(dataframe['cti_20_4h'] < 0.9) item_buy_logic.append(dataframe['rsi_14_4h'] < 80.0) item_buy_logic.append(dataframe['r_480_1h'] < -25.0) item_buy_logic.append(dataframe['r_480_4h'] < -4.0) item_buy_logic.append(dataframe['rsi_3_15m'] > 14.0) item_buy_logic.append(dataframe['rsi_3_1h'] > 16.0) item_buy_logic.append(dataframe['not_downtrend_1h']) item_buy_logic.append(dataframe['not_downtrend_4h']) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) # current 4h red with wick, previous 4h green with wick item_buy_logic.append((dataframe['change_pct_4h'] > -0.02) | (dataframe['top_wick_pct_4h'] < 0.08) | (dataframe['change_pct_4h'].shift(48) < 0.08) | (dataframe['top_wick_pct_4h'].shift(48) < 0.08)) # current 4h green with top wick item_buy_logic.append((dataframe['change_pct_4h'] < 0.02) | (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 2.0)) | (dataframe['high_max_6_1h'] < (dataframe['close'] * 1.2))) # current and previous 4h red item_buy_logic.append((dataframe['change_pct_4h'] > -0.05) | (dataframe['change_pct_4h'].shift(48) > -0.05) | (dataframe['rsi_14_max_6_4h'] < 85.0)) # current 4h red, previous 4h long green with long top wick item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['change_pct_4h'].shift(48) < 0.16) | (dataframe['top_wick_pct_4h'].shift(48) < 0.16) | (dataframe['cti_20_4h'] < 0.8)) # current 4h long green and descending item_buy_logic.append((dataframe['change_pct_4h'] < 0.2) | (dataframe['r_14_4h'] < -20.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(576))) # current 4h green with top wick item_buy_logic.append((dataframe['change_pct_4h'] < 0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['pct_change_high_max_3_12_4h'] > -0.05)) # current 4h red item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['rsi_14_max_6_4h'] < 80.0) | (dataframe['pct_change_high_max_1_12_4h'] > -0.05)) # current 4h red with top wick, previous 4h green item_buy_logic.append((dataframe['change_pct_4h'] > -0.06) | (dataframe['top_wick_pct_4h'] < 0.06) | (dataframe['change_pct_4h'].shift(48) < 0.06) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.3))) item_buy_logic.append((dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['pct_change_high_max_3_12_4h'] > -0.1)) # current 4h red with long top wick, downtrend item_buy_logic.append((dataframe['change_pct_4h'] > -0.02) | (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 5.0)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(576))) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.018)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * 0.996)) # Condition #5 - Normal mode bull. if index == 5: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h']) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.36)) item_buy_logic.append(dataframe['high_max_6_1h'] < (dataframe['close'] * 1.4)) item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)) item_buy_logic.append(dataframe['rsi_14_1h'] < 85.0) item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0) item_buy_logic.append((dataframe['cti_20_4h'] < 0.9) | (dataframe['r_14_4h'] < -30.0)) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h'])) # downtrend 15m, drop in the last 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['close_max_48'] < (dataframe['close'] * 1.2))) # downtrend 1h, drop in the last 1h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['close_max_12'] < (dataframe['close'] * 1.16))) # downtrend 1h, drop in the last 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['close_max_48'] < (dataframe['close'] * 1.2))) # downtrend 1h, drop in the last 4h item_buy_logic.append((dataframe['not_downtrend_4h']) | (dataframe['close_max_48'] < (dataframe['close'] * 1.12))) # current 4h long green, overbought 4h item_buy_logic.append((dataframe['change_pct_4h'] < 0.08) | (dataframe['rsi_14_4h'] < 70.0)) # current 4h with relative long top wick, drop in the last 4h item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0)) | (dataframe['close_max_48'] < (dataframe['close'] * 1.1))) # current 4h red, overbought 4h, drop in the last 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['close_max_48'] < (dataframe['close'] * 1.1))) # current 1d very long green with very long top wick, drop in last 4h, downtrend 4h item_buy_logic.append((dataframe['change_pct_1d'] < 0.3) | (dataframe['top_wick_pct_1d'] < 0.3) | (dataframe['close_max_48'] < (dataframe['close'] * 1.12)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 4h green with top wick, downtrend 4h item_buy_logic.append((dataframe['change_pct_4h'] < 0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 4h red with top wick, downtrend 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 1h red, overbought 1h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1h'] > -0.02) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1h long red, downtrend 1h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1h'] > -0.08) | (dataframe['not_downtrend_1h']) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['rsi_14'] < 36.0) # Condition #6 - Normal mode bull. if index == 6: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h']) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append((dataframe['tpct_change_0'] < 0.03)) item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.36)) item_buy_logic.append(dataframe['high_max_48_4h'] < (dataframe['close'] * 1.9)) item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0) item_buy_logic.append(dataframe['r_14_4h'] < -25.0) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) item_buy_logic.append((dataframe['not_downtrend_15m'])) # downtrend 1h, drop in the last 1h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['close_max_12'] < (dataframe['close'] * 1.16))) # downtrend 1h, downtrend 4h, drop in the last 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 1h, drop in the last 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['close_max_48'] < (dataframe['close'] * 1.2))) # downtrend 1h, drop in the last 4h item_buy_logic.append((dataframe['not_downtrend_4h']) | (dataframe['close_max_48'] < (dataframe['close'] * 1.12))) # current 1h red, overbought 1h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1h'] > -0.02) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h green with top wick, downtrend 1h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_4h'] < 0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h green with top wick, overbought 4h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_4h'] < 0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h red with top wick, overbought 1h, overbought 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['rsi_14_1h'] < 70.0) | (dataframe['rsi_14_4h'] < 70.0)) # downtrend 1d, downtrend 15m, downtrend 1h item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288))) # # downtrend 1d, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 1d, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['cti_20_1d'] < 0.8) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # downtrend 1d, overbought 1d, downtrend 1h, drop in last 2h item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['not_downtrend_1h']) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d red with top wick, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.02) | (dataframe['top_wick_pct_1d'] < 0.02) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h red, overbought 1h, drop in the last 2h item_buy_logic.append((dataframe['change_pct_4h'] > -0.06) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d very long green with very long top wick, drop in last 4h, downtrend 4h item_buy_logic.append((dataframe['change_pct_1d'] < 0.3) | (dataframe['top_wick_pct_1d'] < 0.3) | (dataframe['close_max_48'] < (dataframe['close'] * 1.12)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 1d long green with long top wick, overbought 4h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] < 0.16) | (dataframe['top_wick_pct_1d'] < 0.16) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # overbought 1d, drop in last 2h item_buy_logic.append((dataframe['cti_20_1d'] < 0.9) | (dataframe['rsi_14_1d'] < 75.0) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # overbought 1d, overbought 4h item_buy_logic.append((dataframe['cti_20_1d'] < 0.85) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['cti_20_4h'] < 0.5)) # current 1d long green, down 15m, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] < 0.2) | (dataframe['not_downtrend_15m']) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # downtrend 4h, mild overbought 4h, drop in last 2h and 4h item_buy_logic.append((dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1)) | (dataframe['close_max_48'] < (dataframe['close'] * 1.12))) # Logic item_buy_logic.append(dataframe['close'] < (dataframe['ema_26'] * 0.94)) item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * 0.996)) # Condition #11 - Normal mode bear. if index == 11: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h'] == False) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.16)) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.36)) item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.4)) item_buy_logic.append(dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5)) item_buy_logic.append(dataframe['hl_pct_change_24_1h'] < 0.5) item_buy_logic.append(dataframe['hl_pct_change_48_1h'] < 0.75) item_buy_logic.append(dataframe['ema_50_1h'] > dataframe['ema_200_1h']) item_buy_logic.append(dataframe['cti_20_1h'] < 0.95) item_buy_logic.append(dataframe['rsi_14_1d'] < 80.0) item_buy_logic.append(dataframe['r_14_1h'] < -25.0) item_buy_logic.append(dataframe['r_14_4h'] < -25.0) # curent 4h long red, overbought 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.08) | (dataframe['cti_20_4h'] < 0.85)) # current 1d red with top wick, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] > -0.04) | (dataframe['top_wick_pct_1d'] < 0.04) | (dataframe['rsi_14_1d'] < 70.0)) # current 1d long green, current 4h red with top wick, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] < 0.2) | (dataframe['change_pct_4h'] > -0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['cti_20_1d'] < 0.8)) # current 4h red with top wick, drop in last 4 hours item_buy_logic.append((dataframe['change_pct_4h'] > -0.08) | (dataframe['top_wick_pct_4h'] < 0.08) | (dataframe['close_max_48'] < (dataframe['close'] * 1.16))) # current 4h long red, drop in the last 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.1) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close_max_48'] < (dataframe['close'] * 1.12))) # current 4h green with top wick, current 1d red, overbought 1d item_buy_logic.append((dataframe['change_pct_4h'] < 0.04) | (dataframe['top_wick_pct_4h'] < 0.08) | (dataframe['change_pct_1d'] > -0.04) | (dataframe['cti_20_1d'] < 0.85)) # current 4h green with top wick, previous high is higher than current high item_buy_logic.append((dataframe['change_pct_4h'] < 0.01) | (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 3.0)) | (dataframe['pct_change_high_max_3_24_4h'] > -0.1)) # current 4h with relative long top wick, drop in the last 4h item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0)) | (dataframe['close_max_48'] < (dataframe['close'] * 1.1))) #current 4h red, current higher lower than previous high, downtrend 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['pct_change_high_max_3_24_4h'] > -0.12) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 4h red, overbought 4h, downtrend 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 4h long red item_buy_logic.append((dataframe['change_pct_4h'] > -0.12) | (dataframe['close_max_48'] < (dataframe['close'] * 1.16)) | (dataframe['cti_20_1h'] < -0.5)) # current 1d long green with top long wick item_buy_logic.append((dataframe['change_pct_1d'] < 0.12) | (dataframe['top_wick_pct_1d'] < 0.12)) # current 1d long red, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] > -0.12) | (dataframe['cti_20_1d'] < 0.85)) # current 1d red with top wick, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] > -0.06) | (dataframe['top_wick_pct_1d'] < 0.06) | (dataframe['cti_20_1d'] < 0.9)) # current 1d long red, overbought 1d, drop in the last 4h item_buy_logic.append((dataframe['change_pct_1d'] > -0.04) | (dataframe['cti_20_1d'] < 0.9) | (dataframe['close_max_48'] < (dataframe['close'] * 1.12))) # current 1d red, previous 1d red with top wick item_buy_logic.append((dataframe['change_pct_1d'] > -0.04) | (dataframe['change_pct_1d'].shift(288) > -0.04) | (dataframe['top_wick_pct_1d'].shift(288) < 0.04) | (dataframe['cti_20_1d'] < 0.5)) # current 1d red, previous 1d red with top wick item_buy_logic.append((dataframe['change_pct_1d'] > -0.02) | (dataframe['change_pct_1d'].shift(288) > -0.02) | (dataframe['top_wick_pct_1d'].shift(288) < 0.02) | (dataframe['rsi_14_1d'] < 70.0)) # current 1d green with top wick item_buy_logic.append((dataframe['change_pct_1d'] < 0.04) | (dataframe['top_wick_pct_1d'] < 0.04) | (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['is_downtrend_3_1h'] == False)) # current 1d red, previous 1d red, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] > -0.0) | (dataframe['change_pct_1d'].shift(288) > -0.0) | (dataframe['cti_20_1d'] < 0.9)) # current 1d green, overbought 4h item_buy_logic.append((dataframe['change_pct_1d'] < 0.12) | (dataframe['cti_20_4h'] < 0.8)) # drop while near there was overbought 4h item_buy_logic.append((dataframe['close_max_48'] < (dataframe['close'] * 1.12)) | (dataframe['rsi_14_max_6_4h'] < 80.0) | (dataframe['cti_20_4h'] < 0.5)) # current 4h downtrend, drop in last 24h item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.2))) # current 4h downtrend, overbought 4h item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_4h'] < 0.85)) item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['change_pct_1d'] < 0.12)) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0)) # current 1d long top wick item_buy_logic.append(dataframe['top_wick_pct_1d'] < (abs(dataframe['change_pct_1d']) * 10.0)) item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False) | (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_4h'] < 0.5)) item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False) | (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1d'] < 0.75)) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.016)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * 0.999)) # Condition #12 - Normal mode bear. if index == 12: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h'] == False) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append((dataframe['tpct_change_0'] < 0.034)) item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.26)) item_buy_logic.append(dataframe['high_max_6_1h'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.36)) item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.4)) item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)) item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(48)) item_buy_logic.append(dataframe['cti_20_1h'] < 0.5) item_buy_logic.append(dataframe['cti_20_4h'] < 0.95) item_buy_logic.append(dataframe['rsi_14_1d'] < 80.0) item_buy_logic.append(dataframe['r_14_4h'] < -25.0) item_buy_logic.append(dataframe['not_downtrend_15m']) # current 4h green with top wick item_buy_logic.append((dataframe['change_pct_4h'] < 0.04) | (dataframe['top_wick_pct_4h'] < 0.04)) # current 4h red, overbought, 4h downtrend item_buy_logic.append((dataframe['change_pct_4h'] > -0.06) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 4h long red item_buy_logic.append((dataframe['change_pct_4h'] > -0.1) | (dataframe['cti_20_4h'] < 0.85)) # current 4h red with top wick item_buy_logic.append((dataframe['change_pct_4h'] > -0.02) | (dataframe['top_wick_pct_4h'] < 0.02) | (dataframe['cti_20_4h'] < 0.85)) # 3 4h red item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_4h'] < 0.5)) # current and previous 4h red item_buy_logic.append((dataframe['change_pct_4h'] > -0.0) | (dataframe['change_pct_4h'].shift(48) > -0.0) | (dataframe['rsi_14_4h'] < 50.0) | (dataframe['cti_20_4h'] < 0.5)) item_buy_logic.append((dataframe['hl_pct_change_48_1h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # currend 1d red with top wick item_buy_logic.append((dataframe['change_pct_1d'] > -0.12) | (dataframe['top_wick_pct_1d'] < 0.12)) # currend 1d green with top wick item_buy_logic.append((dataframe['change_pct_1d'] < 0.12) | (dataframe['top_wick_pct_1d'] < 0.12)) # currend 1d red with top wick item_buy_logic.append((dataframe['change_pct_1d'] > -0.06) | (dataframe['top_wick_pct_1d'] < 0.06) | (dataframe['rsi_14_1d'] < 70.0)) # current 1d green with top wick, current 4h red item_buy_logic.append((dataframe['change_pct_1d'] < 0.06) | (dataframe['top_wick_pct_1d'] < 0.06) | (dataframe['change_pct_4h'] > -0.06) | (dataframe['cti_20_4h'] < 0.8)) # current 1d red item_buy_logic.append((dataframe['change_pct_1d'] > -0.05) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['cti_20_1d'] < 0.85)) # current 1d red item_buy_logic.append((dataframe['change_pct_1d'] > -0.12) | (dataframe['cti_20_1d'] < 0.8)) # Logic item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.04)) item_buy_logic.append(dataframe['close_delta'].gt(dataframe['close'] * 0.02)) item_buy_logic.append(dataframe['bb40_2_tail'].lt(dataframe['bb40_2_delta'] * 0.2)) item_buy_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift())) item_buy_logic.append(dataframe['close'].le(dataframe['close'].shift())) # Condition #13 - Normal mode bear. if index == 13: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h'] == False) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.26)) item_buy_logic.append(dataframe['ema_12_1h'] > dataframe['ema_200_1h']) item_buy_logic.append(dataframe['ema_12_4h'] > dataframe['ema_200_4h']) item_buy_logic.append(dataframe['rsi_14_4h'] < 75.0) item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0) item_buy_logic.append(dataframe['not_downtrend_15m']) item_buy_logic.append(dataframe['not_downtrend_1h']) item_buy_logic.append(dataframe['not_downtrend_4h']) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) # current 4h green with wick, overbought 4h item_buy_logic.append((dataframe['change_pct_4h'] < 0.1) | (dataframe['top_wick_pct_4h'] < 0.16) | (dataframe['rsi_14_4h'] < 70.0)) # current 4h long green, overbought 4h item_buy_logic.append((dataframe['change_pct_4h'] < 0.12) | (dataframe['rsi_14_4h'] < 70.0)) # current 4h red with top wick item_buy_logic.append((dataframe['change_pct_4h'] > 0.0) | (dataframe['top_wick_pct_4h'] < 0.1) | (dataframe['ema_12_4h'] > dataframe['ema_200_4h'])) # current 4h long red item_buy_logic.append((dataframe['change_pct_4h'] > -0.1) | (dataframe['rsi_14_max_6_4h'] < 85.0)) # current 4h very long top wick item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0)) | (dataframe['rsi_14_max_6_4h'] < 80.0)) # current 4h red with top wick item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['top_wick_pct_4h'] < 0.06) | (dataframe['rsi_14_max_6_4h'] < 80.0)) # current 4h green with top wick item_buy_logic.append((dataframe['change_pct_4h'] < 0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['rsi_14_4h'] < 70.00)) # current 1d long red, previous 1d long green with long top wick item_buy_logic.append((dataframe['change_pct_1d'] > -0.2) | (dataframe['change_pct_1d'].shift(288) < 0.2) | (dataframe['top_wick_pct_1d'].shift(288) < 0.2)) # current 1d green, overbought 4h item_buy_logic.append((dataframe['change_pct_1d'] < 0.12) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_4h'] < 0.8)) # current 1d long green with long green wick item_buy_logic.append((dataframe['change_pct_1d'] < 0.2) | (dataframe['top_wick_pct_1d'] < 0.2)) # current 1d long green, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] < 0.12) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['cti_20_1d'] < 0.8)) # Logic item_buy_logic.append(dataframe['rsi_14'] < 36.0) item_buy_logic.append(dataframe['ha_close'] > dataframe['ha_open']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.018)) # Condition #14 - Normal mode bear. if index == 14: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h'] == False) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.36)) item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.4)) item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5)) item_buy_logic.append(dataframe['cti_20_1h'] < 0.9) item_buy_logic.append(dataframe['cti_20_4h'] < 0.9) item_buy_logic.append(dataframe['rsi_14_4h'] < 80.0) item_buy_logic.append(dataframe['r_480_1h'] < -25.0) item_buy_logic.append(dataframe['r_480_4h'] < -4.0) item_buy_logic.append(dataframe['rsi_3_15m'] > 14.0) item_buy_logic.append(dataframe['rsi_3_1h'] > 16.0) item_buy_logic.append(dataframe['not_downtrend_1h']) item_buy_logic.append(dataframe['not_downtrend_4h']) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) # current 4h red with wick, previous 4h green with wick item_buy_logic.append((dataframe['change_pct_4h'] > -0.02) | (dataframe['top_wick_pct_4h'] < 0.08) | (dataframe['change_pct_4h'].shift(48) < 0.08) | (dataframe['top_wick_pct_4h'].shift(48) < 0.08)) # current 4h green with top wick item_buy_logic.append((dataframe['change_pct_4h'] < 0.02) | (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 2.0)) | (dataframe['high_max_6_1h'] < (dataframe['close'] * 1.2))) # current and previous 4h red item_buy_logic.append((dataframe['change_pct_4h'] > -0.05) | (dataframe['change_pct_4h'].shift(48) > -0.05) | (dataframe['rsi_14_max_6_4h'] < 85.0)) # current 4h red, previous 4h long green with long top wick item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['change_pct_4h'].shift(48) < 0.16) | (dataframe['top_wick_pct_4h'].shift(48) < 0.16) | (dataframe['cti_20_4h'] < 0.8)) # current 4h long green and descending item_buy_logic.append((dataframe['change_pct_4h'] < 0.2) | (dataframe['r_14_4h'] < -20.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(576))) # current 4h green with top wick item_buy_logic.append((dataframe['change_pct_4h'] < 0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['pct_change_high_max_3_12_4h'] > -0.05)) # current 4h red item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['rsi_14_max_6_4h'] < 80.0) | (dataframe['pct_change_high_max_1_12_4h'] > -0.05)) # current 4h red with top wick, previous 4h green item_buy_logic.append((dataframe['change_pct_4h'] > -0.06) | (dataframe['top_wick_pct_4h'] < 0.06) | (dataframe['change_pct_4h'].shift(48) < 0.06) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.3))) item_buy_logic.append((dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['pct_change_high_max_3_12_4h'] > -0.1)) # current 4h red with long top wick, downtrend item_buy_logic.append((dataframe['change_pct_4h'] > -0.02) | (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 5.0)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(576))) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.018)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * 0.996)) # Condition #15 - Normal mode bear. if index == 15: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h'] == False) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.36)) item_buy_logic.append(dataframe['high_max_6_1h'] < (dataframe['close'] * 1.4)) item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)) item_buy_logic.append(dataframe['rsi_14_1h'] < 85.0) item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0) item_buy_logic.append((dataframe['cti_20_4h'] < 0.9) | (dataframe['r_14_4h'] < -30.0)) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h'])) # downtrend 15m, drop in the last 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['close_max_48'] < (dataframe['close'] * 1.2))) # downtrend 1h, drop in the last 1h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['close_max_12'] < (dataframe['close'] * 1.16))) # downtrend 1h, drop in the last 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['close_max_48'] < (dataframe['close'] * 1.2))) # downtrend 1h, drop in the last 4h item_buy_logic.append((dataframe['not_downtrend_4h']) | (dataframe['close_max_48'] < (dataframe['close'] * 1.12))) # current 4h long green, overbought 4h item_buy_logic.append((dataframe['change_pct_4h'] < 0.08) | (dataframe['rsi_14_4h'] < 70.0)) # current 4h with relative long top wick, drop in the last 4h item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0)) | (dataframe['close_max_48'] < (dataframe['close'] * 1.1))) # current 4h red, overbought 4h, drop in the last 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['close_max_48'] < (dataframe['close'] * 1.1))) # current 1d very long green with very long top wick, drop in last 4h, downtrend 4h item_buy_logic.append((dataframe['change_pct_1d'] < 0.3) | (dataframe['top_wick_pct_1d'] < 0.3) | (dataframe['close_max_48'] < (dataframe['close'] * 1.12)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 4h green with top wick, downtrend 4h item_buy_logic.append((dataframe['change_pct_4h'] < 0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 4h red with top wick, downtrend 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 1h red, overbought 1h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1h'] > -0.02) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1h long red, downtrend 1h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1h'] > -0.08) | (dataframe['not_downtrend_1h']) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['rsi_14'] < 36.0) # Condition #16 - Normal mode bear. if index == 16: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h'] == False) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append((dataframe['tpct_change_0'] < 0.03)) item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.36)) item_buy_logic.append(dataframe['high_max_48_4h'] < (dataframe['close'] * 1.9)) item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0) item_buy_logic.append(dataframe['r_14_4h'] < -25.0) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) item_buy_logic.append((dataframe['not_downtrend_15m'])) # downtrend 1h, drop in the last 1h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['close_max_12'] < (dataframe['close'] * 1.16))) # downtrend 1h, downtrend 4h, drop in the last 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 1h, drop in the last 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['close_max_48'] < (dataframe['close'] * 1.2))) # downtrend 1h, drop in the last 4h item_buy_logic.append((dataframe['not_downtrend_4h']) | (dataframe['close_max_48'] < (dataframe['close'] * 1.12))) # current 1h red, overbought 1h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1h'] > -0.02) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h green with top wick, downtrend 1h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_4h'] < 0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h green with top wick, overbought 4h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_4h'] < 0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h red with top wick, overbought 1h, overbought 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['rsi_14_1h'] < 70.0) | (dataframe['rsi_14_4h'] < 70.0)) # downtrend 1d, downtrend 15m, downtrend 1h item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288))) # # downtrend 1d, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 1d, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['cti_20_1d'] < 0.8) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # downtrend 1d, overbought 1d, downtrend 1h, drop in last 2h item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['not_downtrend_1h']) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d red with top wick, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.02) | (dataframe['top_wick_pct_1d'] < 0.02) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h red, overbought 1h, drop in the last 2h item_buy_logic.append((dataframe['change_pct_4h'] > -0.06) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d very long green with very long top wick, drop in last 4h, downtrend 4h item_buy_logic.append((dataframe['change_pct_1d'] < 0.3) | (dataframe['top_wick_pct_1d'] < 0.3) | (dataframe['close_max_48'] < (dataframe['close'] * 1.12)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 1d long green with long top wick, overbought 4h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] < 0.16) | (dataframe['top_wick_pct_1d'] < 0.16) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # overbought 1d, drop in last 2h item_buy_logic.append((dataframe['cti_20_1d'] < 0.9) | (dataframe['rsi_14_1d'] < 75.0) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # overbought 1d, overbought 4h item_buy_logic.append((dataframe['cti_20_1d'] < 0.85) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['cti_20_4h'] < 0.5)) # current 1d long green, down 15m, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] < 0.2) | (dataframe['not_downtrend_15m']) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # downtrend 4h, mild overbought 4h, drop in last 2h and 4h item_buy_logic.append((dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1)) | (dataframe['close_max_48'] < (dataframe['close'] * 1.12))) # Logic item_buy_logic.append(dataframe['close'] < (dataframe['ema_26'] * 0.94)) item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * 0.996)) # Condition #21 - Pump mode bull. if index == 21: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h']) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['high_max_6_1h'] < (dataframe['close'] * 1.24)) item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(96)) item_buy_logic.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) item_buy_logic.append(dataframe['sma_200_4h'] > dataframe['sma_200_4h'].shift(96)) item_buy_logic.append(dataframe['close'] > dataframe['ema_200']) item_buy_logic.append(dataframe['cti_20_1h'] < 0.9) item_buy_logic.append(dataframe['cti_20_4h'] < 0.88) item_buy_logic.append(dataframe['rsi_14_1h'] < 80.0) item_buy_logic.append(dataframe['rsi_14_4h'] < 80.0) item_buy_logic.append(dataframe['r_480_4h'] < -5.0) item_buy_logic.append(dataframe['not_downtrend_15m']) item_buy_logic.append(dataframe['not_downtrend_1h']) item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False) | (dataframe['rsi_3_1h'] > 20.0)) item_buy_logic.append(dataframe['is_downtrend_5_1h'] == False) item_buy_logic.append(dataframe['not_downtrend_4h']) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) # current 4h red with top wick, previous 4h long green item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 2.0)) | (dataframe['change_pct_4h'].shift(48) < 0.12)) # current 4h red, previous 4h green with wick item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['change_pct_4h'].shift(48) < 0.18) | (dataframe['top_wick_pct_4h'].shift(48) < 0.08) | (dataframe['rsi_14_max_6_4h'] < 80.0)) item_buy_logic.append((dataframe['cti_20_4h'] < 0.7) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.3))) # 4h long top wick item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0)) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(576))) # current 4h red, previous 4h red, 2nd previous 4h big green item_buy_logic.append((dataframe['change_pct_4h'] > -0.02) | (dataframe['change_pct_4h'].shift(48) > -0.02) | (dataframe['change_pct_4h'].shift(96) < 0.2) | (dataframe['hl_pct_change_24_1h'] < 0.5)) # current 4h red with top wick item_buy_logic.append((dataframe['change_pct_4h'] > -0.01) | (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0)) | (dataframe['pct_change_high_max_3_36_4h'] > -0.1)) # current 4h red with top wick item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['top_wick_pct_4h'] < 0.06) | (dataframe['rsi_14_max_6_4h'] < 80.0)) item_buy_logic.append((dataframe['change_pct_4h'] < 0.1) | (dataframe['top_wick_pct_4h'] < 0.08)) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.022)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['rsi_14'] < 36.0) # Condition #22 - Pump mode bull. if index == 22: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h']) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.36)) item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.4)) item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(96)) item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) item_buy_logic.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) item_buy_logic.append(dataframe['cti_20_1h'] < 0.9) item_buy_logic.append(dataframe['cti_20_4h'] < 0.9) item_buy_logic.append(dataframe['rsi_14_1h'] < 70.0) item_buy_logic.append(dataframe['rsi_14_4h'] < 70.0) item_buy_logic.append(dataframe['r_480_4h'] < -25.0) item_buy_logic.append(dataframe['not_downtrend_15m']) item_buy_logic.append(dataframe['not_downtrend_1h']) item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False) | (dataframe['rsi_3_1h'] > 20.0)) item_buy_logic.append(dataframe['is_downtrend_5_1h'] == False) item_buy_logic.append(dataframe['not_downtrend_4h']) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.2) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.01) item_buy_logic.append(dataframe['pct_change_high_max_3_24_4h'] > -0.05) item_buy_logic.append(dataframe['pct_change_high_max_3_48_4h'] > -0.1) item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0)) | (dataframe['r_480_1h'] < -25.0)) # current 4h green with top wick item_buy_logic.append((dataframe['change_pct_4h'] < 0.05) | (dataframe['top_wick_pct_4h'] < 0.05) | (dataframe['cti_20_1h'] < 0.8)) item_buy_logic.append((dataframe['change_pct_4h'] < 0.05) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['r_14_4h'] < -16.0)) # current 4h long top wick item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0)) | (dataframe['cti_20_4h'] < 0.8)) # Logic item_buy_logic.append(dataframe['close'] < (dataframe['ema_16'] * 0.968)) item_buy_logic.append(dataframe['cti_20'] < -0.8) item_buy_logic.append(dataframe['rsi_14'] < 50.0) # Condition #31 - Pump mode bear. if index == 31: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h'] == False) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['high_max_6_1h'] < (dataframe['close'] * 1.24)) item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(96)) item_buy_logic.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) item_buy_logic.append(dataframe['sma_200_4h'] > dataframe['sma_200_4h'].shift(96)) item_buy_logic.append(dataframe['close'] > dataframe['ema_200']) item_buy_logic.append(dataframe['cti_20_1h'] < 0.9) item_buy_logic.append(dataframe['cti_20_4h'] < 0.88) item_buy_logic.append(dataframe['rsi_14_1h'] < 80.0) item_buy_logic.append(dataframe['rsi_14_4h'] < 80.0) item_buy_logic.append(dataframe['r_480_4h'] < -5.0) item_buy_logic.append(dataframe['not_downtrend_15m']) item_buy_logic.append(dataframe['not_downtrend_1h']) item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False) | (dataframe['rsi_3_1h'] > 20.0)) item_buy_logic.append(dataframe['is_downtrend_5_1h'] == False) item_buy_logic.append(dataframe['not_downtrend_4h']) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) # current 4h red with top wick, previous 4h long green item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 2.0)) | (dataframe['change_pct_4h'].shift(48) < 0.12)) # current 4h red, previous 4h green with wick item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['change_pct_4h'].shift(48) < 0.18) | (dataframe['top_wick_pct_4h'].shift(48) < 0.08) | (dataframe['rsi_14_max_6_4h'] < 80.0)) item_buy_logic.append((dataframe['cti_20_4h'] < 0.7) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.3))) # 4h long top wick item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0)) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(576))) # current 4h red, previous 4h red, 2nd previous 4h big green item_buy_logic.append((dataframe['change_pct_4h'] > -0.02) | (dataframe['change_pct_4h'].shift(48) > -0.02) | (dataframe['change_pct_4h'].shift(96) < 0.2) | (dataframe['hl_pct_change_24_1h'] < 0.5)) # current 4h red with top wick item_buy_logic.append((dataframe['change_pct_4h'] > -0.01) | (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0)) | (dataframe['pct_change_high_max_3_36_4h'] > -0.1)) # current 4h red with top wick item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['top_wick_pct_4h'] < 0.06) | (dataframe['rsi_14_max_6_4h'] < 80.0)) item_buy_logic.append((dataframe['change_pct_4h'] < 0.1) | (dataframe['top_wick_pct_4h'] < 0.08)) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.022)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['rsi_14'] < 36.0) # Condition #32 - Pump mode bear. if index == 32: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h'] == False) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.36)) item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.4)) item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(96)) item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) item_buy_logic.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) item_buy_logic.append(dataframe['cti_20_1h'] < 0.9) item_buy_logic.append(dataframe['cti_20_4h'] < 0.9) item_buy_logic.append(dataframe['rsi_14_1h'] < 70.0) item_buy_logic.append(dataframe['rsi_14_4h'] < 70.0) item_buy_logic.append(dataframe['r_480_4h'] < -25.0) item_buy_logic.append(dataframe['not_downtrend_15m']) item_buy_logic.append(dataframe['not_downtrend_1h']) item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False) | (dataframe['rsi_3_1h'] > 20.0)) item_buy_logic.append(dataframe['is_downtrend_5_1h'] == False) item_buy_logic.append(dataframe['not_downtrend_4h']) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.2) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.01) item_buy_logic.append(dataframe['pct_change_high_max_3_24_4h'] > -0.05) item_buy_logic.append(dataframe['pct_change_high_max_3_48_4h'] > -0.1) item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0)) | (dataframe['r_480_1h'] < -25.0)) # current 4h green with top wick item_buy_logic.append((dataframe['change_pct_4h'] < 0.05) | (dataframe['top_wick_pct_4h'] < 0.05) | (dataframe['cti_20_1h'] < 0.8)) item_buy_logic.append((dataframe['change_pct_4h'] < 0.05) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['r_14_4h'] < -16.0)) # current 4h long top wick item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0)) | (dataframe['cti_20_4h'] < 0.8)) # Logic item_buy_logic.append(dataframe['close'] < (dataframe['ema_16'] * 0.968)) item_buy_logic.append(dataframe['cti_20'] < -0.8) item_buy_logic.append(dataframe['rsi_14'] < 50.0) # Condition #41 - Quick mode bull. if index == 41: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h']) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5)) item_buy_logic.append(dataframe['cti_20_1h'] < -0.75) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) # current 1d long red, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] > -0.16) | (dataframe['cti_20_1d'] < 0.85)) # downtrend 4h, downtrend 1h, downtrend 15, drop in last 4 days item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_15m']) | (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.24))) # downtrend4h, downtrend 1h item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['not_downtrend_1h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 4h, downtrend 1h, overbought 4h item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_4h'] < 0.5)) # downtrend 4h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.85)) # downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 1d red, previous 1d red, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['change_pct_1d'] > -0.02) | (dataframe['change_pct_1d'].shift(288) > -0.02) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 1d relative long top wick, overbought 1d, downtrend 1h item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0)) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['not_downtrend_1h'])) # current 1d red, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['change_pct_1d'] > -0.06) | (dataframe['not_downtrend_1h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 1d red, overbought 1d, current 4h red, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['change_pct_1d'] > -0.06) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['change_pct_4h'] > -0.06) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h'])) # current 1d green with top wick, overbought 1d, overbought 4h item_buy_logic.append((dataframe['change_pct_1d'] < 0.04) | (dataframe['top_wick_pct_1d'] < 0.04) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['cti_20_4h'] < 0.85)) # current 4h green, overbought 4h, overbought 1d item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_1d'] < 85.0)) # Logic item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.036)) item_buy_logic.append(dataframe['close_delta'].gt(dataframe['close'] * 0.02)) item_buy_logic.append(dataframe['bb40_2_tail'].lt(dataframe['bb40_2_delta'] * 0.4)) item_buy_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift())) item_buy_logic.append(dataframe['close'].le(dataframe['close'].shift())) item_buy_logic.append(dataframe['rsi_14'] < 36.0) # Condition #42 - Quick mode bull. if index == 42: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h']) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.24)) item_buy_logic.append(dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5)) item_buy_logic.append(dataframe['cti_20_1h'] < -0.0) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) # downtrend 15m, downtrend 1h, downtrend 4h, drop in last 4 days item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.2))) # downtrend 15m, downtrend 1h, CTI 1h not low enough item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5)) # downtrend 1h, CTI 1h not low enough, overbought 1d item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_1d'] < 0.9) | (dataframe['rsi_14_1d'] < 75.0)) # current and previous 4h red, downtrend 4h, downtrend 1h, downtrend 15m item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['change_pct_4h'].shift(48) > -0.04) | (dataframe['is_downtrend_3_4h'] == False) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_15m'])) # downtrend 4h, current 1d long red, overbought 1d item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['change_pct_1d'] > -0.12) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['rsi_14_1d'] < 70.0)) # downtrend 4h, overbought 4h, downtrend 15m, downtrend 1h, drop in last 2h item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['close_max_24'] < (dataframe['close'] * 1.16))) # downtrend 4h, overbought 4h, overbought 1d, downtrend 1h item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['not_downtrend_1h'])) # current 4h long relative top wick, overbought 1d, downtrend 15m item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0)) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['not_downtrend_15m'])) # current 4h long relative top wick, overbought 4h. overbought 15m item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0)) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['not_downtrend_15m'])) # current 1h long red, overbought 4h, drop 15m, rapid drop in RSI 1h item_buy_logic.append((dataframe['change_pct_1h'] > -0.06) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['not_downtrend_15m']) | (((dataframe['rsi_14_1h']) / (dataframe['rsi_14_1h'].shift(12))) > 0.5)) # current 4h red, downtrend 1h, downtrend 4h, drop in last 48h item_buy_logic.append((dataframe['change_pct_4h'] > -0.06) | (dataframe['not_downtrend_1h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_48'] < (dataframe['close'] * 1.16))) # current 4h red with top wick, overbought 4h, overbought 1d, downtrend 1h, downtrend 15m item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_15m'])) # current 4h red with top wick, downtrend 1h, downtrend 2h, drop in last 2h item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d red, downtrend 15m, downtrend 1h, downtrend 4h, CTI 4h not low enough. drop in last 4 days item_buy_logic.append((dataframe['change_pct_1d'] > -0.12) | (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.3))) # downtrend 1h, downtrend 4h, CTI 1h not low enough, drop in last 4 days item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.3))) # overbought 1d, overbought 4h, downtrend 15m item_buy_logic.append((dataframe['cti_20_1d'] < 0.85) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['not_downtrend_15m'])) # currend 1d very long green, overbought 1d, overbought 4h, downtrend 1h, CTI 1h not low enouigh item_buy_logic.append((dataframe['change_pct_1d'] < 0.3) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5)) # downtrend 15m, downtrend 1h, downtrend 4h, overbought 1d item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1d'] < 0.85)) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.018)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * 0.996)) item_buy_logic.append(dataframe['rsi_14'] < 40.0) # Condition #43 - Quick mode bull. if index == 43: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h']) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) # overbought 15m, overbought 1h, overbought 4h item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['rsi_14_1h'] < 50.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['rsi_14_4h'] < 50.0)) # downtrend 15m, overbought 15m, overbought 15m, overbought 4h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 15m, overbought 1h, overbought 15m, overbought 4h,downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 15m, downtrend 1h, overbought 1h, overbought 4h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['rsi_14_1h'] < 36.0) | (dataframe['cti_20_4h'] < -0.85) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_4h'] < 40.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))) # downtrend 15m, overbought 15m, overbought 1h, overbought 4h, drop in last 48h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['close_max_48'] < (dataframe['close'] * 1.16))) # overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['rsi_14_1d'] < 75.0)) # downtrend 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0)) # downtrend 15m, overbought 15m, overbought 1h, overbought 4h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # overbought 15m, overbought 1h, overbought 4h, drop in last 4h item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close_max_48'] < (dataframe['close'] * 1.24))) # downtrend 15m, downtrend 1h, downtrend 4h, overbought 15m, overbought 1h, overbought 4h, drop in last 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['close_max_48'] < (dataframe['close'] * 1.24))) # downtrend 15m, overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['rsi_14_1d'] < 80.0)) # downtrend 15m, downtrend 1h, downtrend 4h, overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.92) | (dataframe['cti_20_4h'] < -0.9) | (dataframe['cti_20_1d'] < -0.0)) # downtrend 1h, overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['rsi_14_1d'] < 80.0)) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 15m, overbought 15m, overbought 1h, overbought 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_4h'] < 0.70)) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, drop in last 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['close_max_48'] < (dataframe['close'] * 1.16))) # downtrend 15m, overbought 15m, overbought 1h, overbought 4h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.16))) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.16))) # downtrend 15m, downtrend 1h, downtrend 4h, overbought 15m, overbought 1h, overbought 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_4h'] < -0.9) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 4h, overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['rsi_14_1d'] < 70.0)) # downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 15m, overbought 15m, overbought 1h, overbought 4h, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['close_max_24'] < (dataframe['close'] * 1.2))) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5)) # overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['rsi_14_1d'] < 80.0)) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 1h, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # downtrend 15m, overbought 15m, overbought 1h, overbought 4h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # downtrend 15m, overbought 15m, overbought 1h, overbought 4h, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # overbought 15m, overbought 1h, overbought 4h, big drop in last 24h item_buy_logic.append((dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.25) | (dataframe['rsi_14_max_6_4h'] < 80.0) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5))) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # overbought 15m, overbought 1h, overbought 4h, downtrend 1h, downtrend 4h, drop in last 4h item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.25) | (dataframe['cti_20_4h'] < -0.25) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_48'] < (dataframe['close'] * 1.16))) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, drop in last 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['close_max_48'] < (dataframe['close'] * 1.2))) # downtrend 15m, downtrend 1h, downtrend 4h, overbought 15m, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # downtrend 15m, overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5)) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0)) # downtrend 1h, downtrend 4h, overbought 15m, overbought 1h, overbought 4h, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.95) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.25) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.85) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))) # current 1d long green with long top wick, downtrend 15m, overbought 15m, overbought 1h, overbought 4hdowntrend 4h item_buy_logic.append((dataframe['change_pct_1d'] < 0.16) | (dataframe['top_wick_pct_1d'] < 0.16) | (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 1h, overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0)) # Logic item_buy_logic.append(dataframe['close'] < (dataframe['ema_26'] * 0.938)) item_buy_logic.append(dataframe['cti_20'] < -0.75) item_buy_logic.append(dataframe['r_14'] < -94.0) # Condition #44 - Quick mode bull. if index == 44: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h']) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.26)) item_buy_logic.append(dataframe['high_max_6_1h'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.36)) item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.4)) item_buy_logic.append(dataframe['high_max_36_1h'] < (dataframe['close'] * 1.46)) item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)) item_buy_logic.append(dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5)) item_buy_logic.append(dataframe['high_max_36_4h'] < (dataframe['close'] * 1.7)) item_buy_logic.append(dataframe['close_max_48'] > (dataframe['close'] * 1.1)) item_buy_logic.append(dataframe['cti_40_1h'] < -0.8) item_buy_logic.append(dataframe['r_96_1h'] < -70.0) item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False) | (dataframe['rsi_3_1h'] > 20.0)) item_buy_logic.append(dataframe['is_downtrend_5_1h'] == False) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['r_480_1h'] > -95.0)) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['r_480_4h'] > -95.0)) item_buy_logic.append((dataframe['not_downtrend_4h']) | (dataframe['r_480_1h'] > -95.0)) item_buy_logic.append((dataframe['not_downtrend_4h']) | (dataframe['r_480_4h'] > -95.0)) item_buy_logic.append(dataframe['pct_change_high_max_3_36_4h'] > -0.5) item_buy_logic.append((dataframe['pct_change_high_max_3_36_4h'] > -0.2) | (dataframe['r_480_4h'] > -80.0)) # Logic item_buy_logic.append(dataframe['bb20_2_width_1h'] > 0.156) item_buy_logic.append(dataframe['cti_20'] < -0.88) item_buy_logic.append(dataframe['r_14'] < -50.0) # Condition #51 - Quick mode bear. if index == 51: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h'] == False) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5)) item_buy_logic.append(dataframe['cti_20_1h'] < -0.75) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) # current 1d long red, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] > -0.16) | (dataframe['cti_20_1d'] < 0.85)) # downtrend 4h, downtrend 1h, downtrend 15, drop in last 4 days item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_15m']) | (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.24))) # downtrend4h, downtrend 1h item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['not_downtrend_1h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 4h, downtrend 1h, overbought 4h item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_4h'] < 0.5)) # downtrend 4h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.85)) # downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 1d red, previous 1d red, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['change_pct_1d'] > -0.02) | (dataframe['change_pct_1d'].shift(288) > -0.02) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 1d relative long top wick, overbought 1d, downtrend 1h item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0)) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['not_downtrend_1h'])) # current 1d red, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['change_pct_1d'] > -0.06) | (dataframe['not_downtrend_1h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 1d red, overbought 1d, current 4h red, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['change_pct_1d'] > -0.06) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['change_pct_4h'] > -0.06) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h'])) # current 1d green with top wick, overbought 1d, overbought 4h item_buy_logic.append((dataframe['change_pct_1d'] < 0.04) | (dataframe['top_wick_pct_1d'] < 0.04) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['cti_20_4h'] < 0.85)) # current 4h green, overbought 4h, overbought 1d item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_1d'] < 85.0)) # Logic item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.036)) item_buy_logic.append(dataframe['close_delta'].gt(dataframe['close'] * 0.02)) item_buy_logic.append(dataframe['bb40_2_tail'].lt(dataframe['bb40_2_delta'] * 0.4)) item_buy_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift())) item_buy_logic.append(dataframe['close'].le(dataframe['close'].shift())) item_buy_logic.append(dataframe['rsi_14'] < 36.0) # Condition #52 - Quick mode bear. if index == 52: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h'] == False) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.24)) item_buy_logic.append(dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5)) item_buy_logic.append(dataframe['cti_20_1h'] < -0.0) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) # downtrend 15m, downtrend 1h, downtrend 4h, drop in last 4 days item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.2))) # downtrend 15m, downtrend 1h, CTI 1h not low enough item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5)) # downtrend 1h, CTI 1h not low enough, overbought 1d item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_1d'] < 0.9) | (dataframe['rsi_14_1d'] < 75.0)) # current and previous 4h red, downtrend 4h, downtrend 1h, downtrend 15m item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['change_pct_4h'].shift(48) > -0.04) | (dataframe['is_downtrend_3_4h'] == False) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_15m'])) # downtrend 4h, current 1d long red, overbought 1d item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['change_pct_1d'] > -0.12) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['rsi_14_1d'] < 70.0)) # downtrend 4h, overbought 4h, downtrend 15m, downtrend 1h, drop in last 2h item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['close_max_24'] < (dataframe['close'] * 1.16))) # downtrend 4h, overbought 4h, overbought 1d, downtrend 1h item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['not_downtrend_1h'])) # current 4h long relative top wick, overbought 1d, downtrend 15m item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0)) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['not_downtrend_15m'])) # current 4h long relative top wick, overbought 4h. overbought 15m item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0)) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['not_downtrend_15m'])) # current 1h long red, overbought 4h, drop 15m, rapid drop in RSI 1h item_buy_logic.append((dataframe['change_pct_1h'] > -0.06) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['not_downtrend_15m']) | (((dataframe['rsi_14_1h']) / (dataframe['rsi_14_1h'].shift(12))) > 0.5)) # current 4h red, downtrend 1h, downtrend 4h, drop in last 48h item_buy_logic.append((dataframe['change_pct_4h'] > -0.06) | (dataframe['not_downtrend_1h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_48'] < (dataframe['close'] * 1.16))) # current 4h red with top wick, overbought 4h, overbought 1d, downtrend 1h, downtrend 15m item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_15m'])) # current 4h red with top wick, downtrend 1h, downtrend 2h, drop in last 2h item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d red, downtrend 15m, downtrend 1h, downtrend 4h, CTI 4h not low enough. drop in last 4 days item_buy_logic.append((dataframe['change_pct_1d'] > -0.12) | (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.3))) # downtrend 1h, downtrend 4h, CTI 1h not low enough, drop in last 4 days item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.3))) # overbought 1d, overbought 4h, downtrend 15m item_buy_logic.append((dataframe['cti_20_1d'] < 0.85) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['not_downtrend_15m'])) # currend 1d very long green, overbought 1d, overbought 4h, downtrend 1h, CTI 1h not low enouigh item_buy_logic.append((dataframe['change_pct_1d'] < 0.3) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5)) # downtrend 15m, downtrend 1h, downtrend 4h, overbought 1d item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1d'] < 0.85)) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.018)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * 0.996)) item_buy_logic.append(dataframe['rsi_14'] < 40.0) # Condition #53 - Quick mode bear. if index == 53: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h'] == False) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) # overbought 15m, overbought 1h, overbought 4h item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['rsi_14_1h'] < 50.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['rsi_14_4h'] < 50.0)) # downtrend 15m, overbought 15m, overbought 15m, overbought 4h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 15m, overbought 1h, overbought 15m, overbought 4h,downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 15m, downtrend 1h, overbought 1h, overbought 4h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['rsi_14_1h'] < 36.0) | (dataframe['cti_20_4h'] < -0.85) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_4h'] < 40.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))) # downtrend 15m, overbought 15m, overbought 1h, overbought 4h, drop in last 48h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['close_max_48'] < (dataframe['close'] * 1.16))) # overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['rsi_14_1d'] < 75.0)) # downtrend 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0)) # downtrend 15m, overbought 15m, overbought 1h, overbought 4h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # overbought 15m, overbought 1h, overbought 4h, drop in last 4h item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close_max_48'] < (dataframe['close'] * 1.24))) # downtrend 15m, downtrend 1h, downtrend 4h, overbought 15m, overbought 1h, overbought 4h, drop in last 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['close_max_48'] < (dataframe['close'] * 1.24))) # downtrend 15m, overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['rsi_14_1d'] < 80.0)) # downtrend 15m, downtrend 1h, downtrend 4h, overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.92) | (dataframe['cti_20_4h'] < -0.9) | (dataframe['cti_20_1d'] < -0.0)) # downtrend 1h, overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['rsi_14_1d'] < 80.0)) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 15m, overbought 15m, overbought 1h, overbought 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_4h'] < 0.70)) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, drop in last 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['close_max_48'] < (dataframe['close'] * 1.16))) # downtrend 15m, overbought 15m, overbought 1h, overbought 4h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.16))) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.16))) # downtrend 15m, downtrend 1h, downtrend 4h, overbought 15m, overbought 1h, overbought 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_4h'] < -0.9) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 4h, overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['rsi_14_1d'] < 70.0)) # downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 15m, overbought 15m, overbought 1h, overbought 4h, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['close_max_24'] < (dataframe['close'] * 1.2))) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5)) # overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['rsi_14_1d'] < 80.0)) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 1h, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # downtrend 15m, overbought 15m, overbought 1h, overbought 4h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # downtrend 15m, overbought 15m, overbought 1h, overbought 4h, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # overbought 15m, overbought 1h, overbought 4h, big drop in last 24h item_buy_logic.append((dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.25) | (dataframe['rsi_14_max_6_4h'] < 80.0) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5))) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # overbought 15m, overbought 1h, overbought 4h, downtrend 1h, downtrend 4h, drop in last 4h item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.25) | (dataframe['cti_20_4h'] < -0.25) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_48'] < (dataframe['close'] * 1.16))) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, drop in last 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['close_max_48'] < (dataframe['close'] * 1.2))) # downtrend 15m, downtrend 1h, downtrend 4h, overbought 15m, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # downtrend 15m, overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5)) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0)) # downtrend 1h, downtrend 4h, overbought 15m, overbought 1h, overbought 4h, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.95) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.25) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.85) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))) # current 1d long green with long top wick, downtrend 15m, overbought 15m, overbought 1h, overbought 4hdowntrend 4h item_buy_logic.append((dataframe['change_pct_1d'] < 0.16) | (dataframe['top_wick_pct_1d'] < 0.16) | (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # downtrend 1h, overbought 15m, overbought 1h, overbought 4h, overbought 1d item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0)) # Logic item_buy_logic.append(dataframe['close'] < (dataframe['ema_26'] * 0.938)) item_buy_logic.append(dataframe['cti_20'] < -0.75) item_buy_logic.append(dataframe['r_14'] < -94.0) # Condition #54 - Quick mode bear. if index == 54: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h'] == False) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.26)) item_buy_logic.append(dataframe['high_max_6_1h'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.36)) item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.4)) item_buy_logic.append(dataframe['high_max_36_1h'] < (dataframe['close'] * 1.46)) item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)) item_buy_logic.append(dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5)) item_buy_logic.append(dataframe['high_max_36_4h'] < (dataframe['close'] * 1.7)) item_buy_logic.append(dataframe['close_max_48'] > (dataframe['close'] * 1.1)) item_buy_logic.append(dataframe['cti_40_1h'] < -0.8) item_buy_logic.append(dataframe['r_96_1h'] < -70.0) item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False) | (dataframe['rsi_3_1h'] > 20.0)) item_buy_logic.append(dataframe['is_downtrend_5_1h'] == False) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['r_480_1h'] > -95.0)) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['r_480_4h'] > -95.0)) item_buy_logic.append((dataframe['not_downtrend_4h']) | (dataframe['r_480_1h'] > -95.0)) item_buy_logic.append((dataframe['not_downtrend_4h']) | (dataframe['r_480_4h'] > -95.0)) item_buy_logic.append(dataframe['pct_change_high_max_3_36_4h'] > -0.5) item_buy_logic.append((dataframe['pct_change_high_max_3_36_4h'] > -0.2) | (dataframe['r_480_4h'] > -80.0)) # Logic item_buy_logic.append(dataframe['bb20_2_width_1h'] > 0.156) item_buy_logic.append(dataframe['cti_20'] < -0.88) item_buy_logic.append(dataframe['r_14'] < -50.0) # Condition #61 - Rebuy mode bull. if index == 61: # Protections item_buy_logic.append(current_free_slots >= self.rebuy_mode_min_free_slots) item_buy_logic.append(dataframe['btc_is_bull_4h']) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.12)) item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.16)) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['high_max_6_1h'] < (dataframe['close'] * 1.24)) item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.36)) item_buy_logic.append(dataframe['hl_pct_change_24_1h'] < 0.5) item_buy_logic.append(dataframe['hl_pct_change_48_1h'] < 0.75) item_buy_logic.append(dataframe['cti_20_1h'] < 0.95) item_buy_logic.append(dataframe['cti_20_4h'] < 0.95) item_buy_logic.append(dataframe['rsi_14_1h'] < 85.0) item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0) item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0) item_buy_logic.append(dataframe['r_14_1h'] < -25.0) item_buy_logic.append(dataframe['r_14_4h'] < -25.0) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) item_buy_logic.append(dataframe['not_downtrend_15m']) # current 1h downtrend, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 1h red, overbought 1h, downtrend 1h, downtrend 1h, drop last 2h item_buy_logic.append((dataframe['change_pct_1h'] > -0.04) | (dataframe['cti_20_1h'] < 0.85) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d green, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] < 0.16) | (dataframe['cti_20_1d'] < 0.5)) # current 1d long relative top wick, overbought 1d, current 4h red, drop last 4h item_buy_logic.append((dataframe['top_wick_pct_1d'] < (abs(dataframe['change_pct_1d']) * 5.0)) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['change_pct_4h'] > -0.04) | (dataframe['close_max_48'] < (dataframe['close'] * 1.1))) # downtrend 1d, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d red with top wick, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.02) | (dataframe['top_wick_pct_1d'] < 0.02) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d green with top wick, downtrend 4h, overbought 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] < 0.06) | (dataframe['top_wick_pct_1d'] < 0.06) | (dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1h red, overbought 1h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1h'] > -0.02) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h grered, previous 4h green, overbought 1h, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['change_pct_4h'].shift(48) < 0.04) | (dataframe['cti_20_1h'] < 0.85) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 4h red, downtrend 1h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['not_downtrend_1h']) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.08))) # current 1d long red, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.16) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d relative long top wick, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['top_wick_pct_1d'] < (abs(dataframe['change_pct_1d']) * 2.0)) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current and previous 1d red, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] > -0.0) | (dataframe['change_pct_1d'].shift(288) > -0.0) | (dataframe['cti_20_1d'] < 0.85)) # downtrend 1d, overbought 1d item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['cti_20_1d'] < 0.5)) # overbought 1d item_buy_logic.append((dataframe['cti_20_1d'] < 0.9) | (dataframe['rsi_14_1d'] < 80.0)) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.016)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['close_delta'] > dataframe['close'] * 12.0 / 1000) item_buy_logic.append(dataframe['rsi_14'] < 30.0) # Condition #71 - Rebuy mode bear. if index == 71: # Protections item_buy_logic.append(current_free_slots >= self.rebuy_mode_min_free_slots) item_buy_logic.append(dataframe['btc_is_bull_4h'] == False) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.12)) item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.16)) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['high_max_6_1h'] < (dataframe['close'] * 1.24)) item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.36)) item_buy_logic.append(dataframe['hl_pct_change_24_1h'] < 0.5) item_buy_logic.append(dataframe['hl_pct_change_48_1h'] < 0.75) item_buy_logic.append(dataframe['cti_20_1h'] < 0.95) item_buy_logic.append(dataframe['cti_20_4h'] < 0.95) item_buy_logic.append(dataframe['rsi_14_1h'] < 85.0) item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0) item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0) item_buy_logic.append(dataframe['r_14_1h'] < -25.0) item_buy_logic.append(dataframe['r_14_4h'] < -25.0) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) item_buy_logic.append(dataframe['not_downtrend_15m']) # current 1h downtrend, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 1h red, overbought 1h, downtrend 1h, downtrend 1h, drop last 2h item_buy_logic.append((dataframe['change_pct_1h'] > -0.04) | (dataframe['cti_20_1h'] < 0.85) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d green, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] < 0.16) | (dataframe['cti_20_1d'] < 0.5)) # current 1d long relative top wick, overbought 1d, current 4h red, drop last 4h item_buy_logic.append((dataframe['top_wick_pct_1d'] < (abs(dataframe['change_pct_1d']) * 5.0)) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['change_pct_4h'] > -0.04) | (dataframe['close_max_48'] < (dataframe['close'] * 1.1))) # downtrend 1d, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d red with top wick, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.02) | (dataframe['top_wick_pct_1d'] < 0.02) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d green with top wick, downtrend 4h, overbought 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] < 0.06) | (dataframe['top_wick_pct_1d'] < 0.06) | (dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1h red, overbought 1h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1h'] > -0.02) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h grered, previous 4h green, overbought 1h, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['change_pct_4h'].shift(48) < 0.04) | (dataframe['cti_20_1h'] < 0.85) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 4h red, downtrend 1h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['not_downtrend_1h']) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.08))) # current 1d long red, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.16) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d relative long top wick, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['top_wick_pct_1d'] < (abs(dataframe['change_pct_1d']) * 2.0)) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current and previous 1d red, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] > -0.0) | (dataframe['change_pct_1d'].shift(288) > -0.0) | (dataframe['cti_20_1d'] < 0.85)) # downtrend 1d, overbought 1d item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['cti_20_1d'] < 0.5)) # overbought 1d item_buy_logic.append((dataframe['cti_20_1d'] < 0.9) | (dataframe['rsi_14_1d'] < 80.0)) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.016)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['close_delta'] > dataframe['close'] * 12.0 / 1000) item_buy_logic.append(dataframe['rsi_14'] < 30.0) # Condition #81 - Long mode bull. if index == 81: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h']) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.12)) item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.16)) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['high_max_6_1h'] < (dataframe['close'] * 1.24)) item_buy_logic.append(dataframe['cti_20_1h'] < 0.95) item_buy_logic.append(dataframe['cti_20_4h'] < 0.95) item_buy_logic.append(dataframe['rsi_14_1h'] < 85.0) item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0) item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0) item_buy_logic.append(dataframe['r_14_1h'] < -25.0) item_buy_logic.append(dataframe['r_14_4h'] < -25.0) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) item_buy_logic.append(dataframe['not_downtrend_15m']) # current 4h relative long top wick, overbought 1h, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 2.0)) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(576))) # current 4h relative long top wick, overbought 1d item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 6.0)) | (dataframe['cti_20_1d'] < 0.5)) # current 4h relative long top wick, overbought 1h, downtrend 1h item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 2.0)) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['not_downtrend_1h'])) # big drop in last 48h, downtrend 1h item_buy_logic.append((dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)) | (dataframe['not_downtrend_1h'])) # downtrend 1h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # downtrend 1h, overbought 1h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < 0.5)) # downtrend 1h, overbought 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_4h'] < 0.5)) # downtrend 1h, downtrend 4h, overbought 1d item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1d'] < 0.5)) # downtrend 1d, overbought 1d item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['cti_20_1d'] < 0.5)) # downtrend 1d, downtrend 1h item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['not_downtrend_1h'])) # current 4h red, previous 4h green, overbought 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.06) | (dataframe['change_pct_4h'].shift(48) < 0.06) | (dataframe['cti_20_4h'] < 0.5)) # current 1d long green with long top wick item_buy_logic.append((dataframe['change_pct_1d'] < 0.12) | (dataframe['top_wick_pct_1d'] < 0.12)) # current 1d long 1d with top wick, overbought 1d, downtrend 1h item_buy_logic.append((dataframe['change_pct_1d'] < 0.2) | (dataframe['top_wick_pct_1d'] < 0.04) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['not_downtrend_1h'])) # current 1d long red, overbought 1d, downtrend 1h item_buy_logic.append((dataframe['change_pct_1d'] > -0.1) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['not_downtrend_1h'])) # Logic item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.052)) item_buy_logic.append(dataframe['close_delta'].gt(dataframe['close'] * 0.024)) item_buy_logic.append(dataframe['bb40_2_tail'].lt(dataframe['bb40_2_delta'] * 0.2)) item_buy_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift())) item_buy_logic.append(dataframe['close'].le(dataframe['close'].shift())) item_buy_logic.append(dataframe['rsi_14'] < 30.0) # Condition #82 - Long mode bull. if index == 82: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h']) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['ema_50_1h'] > dataframe['ema_200_1h']) item_buy_logic.append(dataframe['sma_50_1h'] > dataframe['sma_200_1h']) item_buy_logic.append(dataframe['ema_50_4h'] > dataframe['ema_200_4h']) item_buy_logic.append(dataframe['sma_50_4h'] > dataframe['sma_200_4h']) item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0) item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0) item_buy_logic.append(dataframe['r_480_4h'] < -10.0) # current 1d long green with long top wick item_buy_logic.append((dataframe['change_pct_1d'] < 0.12) | (dataframe['top_wick_pct_1d'] < 0.12)) # overbought 1d, overbought 4h, downtrend 1h, drop in last 2h item_buy_logic.append((dataframe['rsi_14_1d'] < 70.0) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['not_downtrend_1h']) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h red, downtrend 1h, overbought 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_4h'] > -0.06) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h long red, downtrend 1h, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_4h'] > -0.12) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1d'] < 0.8) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d red, overbought 1d, downtrend 1h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.12) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['not_downtrend_1h']) | (dataframe['is_downtrend_3_4h'] == False) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d red, overbought 1d, downtrend 1h, current 4h red, previous 4h green with top wick item_buy_logic.append((dataframe['change_pct_1d'] > -0.08) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['not_downtrend_1h']) | (dataframe['change_pct_4h'] > -0.0) | (dataframe['change_pct_4h'].shift(48) < 0.04) | (dataframe['top_wick_pct_4h'].shift(48) < 0.04)) # current 1d long red with long top wick, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.12) | (dataframe['top_wick_pct_1d'] < 0.12) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d long red, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.16) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h red with top wick, overbought 1d item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['cti_20_1d'] < 0.85)) # current 4h green with top wick, overbought 4h item_buy_logic.append((dataframe['change_pct_4h'] < 0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['rsi_14_4h'] < 70.0)) # current 4h red, downtrend 1h, overbought 1d item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1d'] < 0.5)) # current 1d long relative top wick, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['top_wick_pct_1d'] < (abs(dataframe['change_pct_1d']) * 4.0)) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h relative long top wick, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0)) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['rsi_14_1d'] < 50.0) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current and previous 1d red, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.04) | (dataframe['change_pct_1d'].shift(288) > -0.04) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h long green, overbought 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] < 0.08) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['cti_20'] < -0.8) # Condition #91 - Long mode bear. if index == 91: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h'] == False) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.12)) item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.16)) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['high_max_6_1h'] < (dataframe['close'] * 1.24)) item_buy_logic.append(dataframe['cti_20_1h'] < 0.95) item_buy_logic.append(dataframe['cti_20_4h'] < 0.95) item_buy_logic.append(dataframe['rsi_14_1h'] < 85.0) item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0) item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0) item_buy_logic.append(dataframe['r_14_1h'] < -25.0) item_buy_logic.append(dataframe['r_14_4h'] < -25.0) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) item_buy_logic.append(dataframe['not_downtrend_15m']) # current 4h relative long top wick, overbought 1h, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 2.0)) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(576))) # current 4h relative long top wick, overbought 1d item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 6.0)) | (dataframe['cti_20_1d'] < 0.5)) # current 4h relative long top wick, overbought 1h, downtrend 1h item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 2.0)) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['not_downtrend_1h'])) # big drop in last 48h, downtrend 1h item_buy_logic.append((dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)) | (dataframe['not_downtrend_1h'])) # downtrend 1h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # downtrend 1h, overbought 1h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < 0.5)) # downtrend 1h, overbought 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_4h'] < 0.5)) # downtrend 1h, downtrend 4h, overbought 1d item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1d'] < 0.5)) # downtrend 1d, overbought 1d item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['cti_20_1d'] < 0.5)) # downtrend 1d, downtrend 1h item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['not_downtrend_1h'])) # current 4h red, previous 4h green, overbought 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.06) | (dataframe['change_pct_4h'].shift(48) < 0.06) | (dataframe['cti_20_4h'] < 0.5)) # current 1d long green with long top wick item_buy_logic.append((dataframe['change_pct_1d'] < 0.12) | (dataframe['top_wick_pct_1d'] < 0.12)) # current 1d long 1d with top wick, overbought 1d, downtrend 1h item_buy_logic.append((dataframe['change_pct_1d'] < 0.2) | (dataframe['top_wick_pct_1d'] < 0.04) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['not_downtrend_1h'])) # current 1d long red, overbought 1d, downtrend 1h item_buy_logic.append((dataframe['change_pct_1d'] > -0.1) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['not_downtrend_1h'])) # Logic item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.052)) item_buy_logic.append(dataframe['close_delta'].gt(dataframe['close'] * 0.024)) item_buy_logic.append(dataframe['bb40_2_tail'].lt(dataframe['bb40_2_delta'] * 0.2)) item_buy_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift())) item_buy_logic.append(dataframe['close'].le(dataframe['close'].shift())) item_buy_logic.append(dataframe['rsi_14'] < 30.0) # Condition #92 - Long mode bear. if index == 92: # Protections item_buy_logic.append(dataframe['btc_is_bull_4h'] == False) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['ema_50_1h'] > dataframe['ema_200_1h']) item_buy_logic.append(dataframe['sma_50_1h'] > dataframe['sma_200_1h']) item_buy_logic.append(dataframe['ema_50_4h'] > dataframe['ema_200_4h']) item_buy_logic.append(dataframe['sma_50_4h'] > dataframe['sma_200_4h']) item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0) item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0) item_buy_logic.append(dataframe['r_480_4h'] < -10.0) # current 1d long green with long top wick item_buy_logic.append((dataframe['change_pct_1d'] < 0.12) | (dataframe['top_wick_pct_1d'] < 0.12)) # overbought 1d, overbought 4h, downtrend 1h, drop in last 2h item_buy_logic.append((dataframe['rsi_14_1d'] < 70.0) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['not_downtrend_1h']) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h red, downtrend 1h, overbought 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_4h'] > -0.06) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h long red, downtrend 1h, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_4h'] > -0.12) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1d'] < 0.8) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d red, overbought 1d, downtrend 1h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.12) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['not_downtrend_1h']) | (dataframe['is_downtrend_3_4h'] == False) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d red, overbought 1d, downtrend 1h, current 4h red, previous 4h green with top wick item_buy_logic.append((dataframe['change_pct_1d'] > -0.08) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['not_downtrend_1h']) | (dataframe['change_pct_4h'] > -0.0) | (dataframe['change_pct_4h'].shift(48) < 0.04) | (dataframe['top_wick_pct_4h'].shift(48) < 0.04)) # current 1d long red with long top wick, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.12) | (dataframe['top_wick_pct_1d'] < 0.12) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d long red, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.16) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h red with top wick, overbought 1d item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['cti_20_1d'] < 0.85)) # current 4h green with top wick, overbought 4h item_buy_logic.append((dataframe['change_pct_4h'] < 0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['rsi_14_4h'] < 70.0)) # current 4h red, downtrend 1h, overbought 1d item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1d'] < 0.5)) # current 1d long relative top wick, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['top_wick_pct_1d'] < (abs(dataframe['change_pct_1d']) * 4.0)) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h relative long top wick, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0)) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['rsi_14_1d'] < 50.0) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current and previous 1d red, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.04) | (dataframe['change_pct_1d'].shift(288) > -0.04) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h long green, overbought 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] < 0.08) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['cti_20'] < -0.8) item_buy_logic.append(dataframe['volume'] > 0) item_buy = reduce(lambda x, y: x & y, item_buy_logic) dataframe.loc[item_buy, 'enter_tag'] += f"{index} " conditions.append(item_buy) dataframe.loc[:, 'enter_long'] = item_buy if conditions: dataframe.loc[:, 'enter_long'] = reduce(lambda x, y: x | y, conditions) return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'exit_long'] = 0 dataframe.loc[:, 'exit_short'] = 0 return dataframe def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool: # allow force entries if (entry_tag == 'force_entry'): return True dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if(len(dataframe) < 1): return False dataframe = dataframe.iloc[-1].squeeze() if ((rate > dataframe['close'])): slippage = ((rate / dataframe['close']) - 1.0) if slippage < 0.038: return True else: log.warning( "Cancelling buy for %s due to slippage %s", pair, slippage ) return False return True def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: # Allow force exits if exit_reason != 'force_exit': if self._should_hold_trade(trade, rate, exit_reason): return False if self.exit_profit_only: current_profit = ((rate - trade.open_rate) / trade.open_rate) if (current_profit < self.exit_profit_offset): return False self._remove_profit_target(pair) return True def bot_loop_start(self, **kwargs) -> None: if self.config["runmode"].value not in ("live", "dry_run"): return super().bot_loop_start(**kwargs) if self.hold_support_enabled: self.load_hold_trades_config() return super().bot_loop_start(**kwargs) def _set_profit_target(self, pair: str, sell_reason: str, rate: float, current_profit: float, current_time: datetime): self.target_profit_cache.data[pair] = { "rate": rate, "profit": current_profit, "sell_reason": sell_reason, "time_profit_reached": current_time.isoformat() } self.target_profit_cache.save() def _remove_profit_target(self, pair: str): if self.target_profit_cache is not None: self.target_profit_cache.data.pop(pair, None) self.target_profit_cache.save() def get_hold_trades_config_file(self): proper_holds_file_path = self.config["user_data_dir"].resolve() / "nfi-hold-trades.json" if proper_holds_file_path.is_file(): return proper_holds_file_path strat_file_path = pathlib.Path(__file__) hold_trades_config_file_resolve = strat_file_path.resolve().parent / "hold-trades.json" if hold_trades_config_file_resolve.is_file(): log.warning( "Please move %s to %s which is now the expected path for the holds file", hold_trades_config_file_resolve, proper_holds_file_path, ) return hold_trades_config_file_resolve # The resolved path does not exist, is it a symlink? hold_trades_config_file_absolute = strat_file_path.absolute().parent / "hold-trades.json" if hold_trades_config_file_absolute.is_file(): log.warning( "Please move %s to %s which is now the expected path for the holds file", hold_trades_config_file_absolute, proper_holds_file_path, ) return hold_trades_config_file_absolute def load_hold_trades_config(self): if self.hold_trades_cache is None: hold_trades_config_file = self.get_hold_trades_config_file() if hold_trades_config_file: log.warning("Loading hold support data from %s", hold_trades_config_file) self.hold_trades_cache = HoldsCache(hold_trades_config_file) if self.hold_trades_cache: self.hold_trades_cache.load() def _should_hold_trade(self, trade: "Trade", rate: float, sell_reason: str) -> bool: if self.config['runmode'].value not in ('live', 'dry_run'): return False if not self.hold_support_enabled: return False # Just to be sure our hold data is loaded, should be a no-op call after the first bot loop self.load_hold_trades_config() if not self.hold_trades_cache: # Cache hasn't been setup, likely because the corresponding file does not exist, sell return False if not self.hold_trades_cache.data: # We have no pairs we want to hold until profit, sell return False # By default, no hold should be done hold_trade = False trade_ids: dict = self.hold_trades_cache.data.get("trade_ids") if trade_ids and trade.id in trade_ids: trade_profit_ratio = trade_ids[trade.id] current_profit_ratio = trade.calc_profit_ratio(rate) if sell_reason == "force_sell": formatted_profit_ratio = f"{trade_profit_ratio * 100}%" formatted_current_profit_ratio = f"{current_profit_ratio * 100}%" log.warning( "Force selling %s even though the current profit of %s < %s", trade, formatted_current_profit_ratio, formatted_profit_ratio ) return False elif current_profit_ratio >= trade_profit_ratio: # This pair is on the list to hold, and we reached minimum profit, sell formatted_profit_ratio = f"{trade_profit_ratio * 100}%" formatted_current_profit_ratio = f"{current_profit_ratio * 100}%" log.warning( "Selling %s because the current profit of %s >= %s", trade, formatted_current_profit_ratio, formatted_profit_ratio ) return False # This pair is on the list to hold, and we haven't reached minimum profit, hold hold_trade = True trade_pairs: dict = self.hold_trades_cache.data.get("trade_pairs") if trade_pairs and trade.pair in trade_pairs: trade_profit_ratio = trade_pairs[trade.pair] current_profit_ratio = trade.calc_profit_ratio(rate) if sell_reason == "force_sell": formatted_profit_ratio = f"{trade_profit_ratio * 100}%" formatted_current_profit_ratio = f"{current_profit_ratio * 100}%" log.warning( "Force selling %s even though the current profit of %s < %s", trade, formatted_current_profit_ratio, formatted_profit_ratio ) return False elif current_profit_ratio >= trade_profit_ratio: # This pair is on the list to hold, and we reached minimum profit, sell formatted_profit_ratio = f"{trade_profit_ratio * 100}%" formatted_current_profit_ratio = f"{current_profit_ratio * 100}%" log.warning( "Selling %s because the current profit of %s >= %s", trade, formatted_current_profit_ratio, formatted_profit_ratio ) return False # This pair is on the list to hold, and we haven't reached minimum profit, hold hold_trade = True return hold_trade # +---------------------------------------------------------------------------+ # | Custom Indicators | # +---------------------------------------------------------------------------+ # Range midpoint acts as Support def is_support(row_data) -> bool: conditions = [] for row in range(len(row_data)-1): if row < len(row_data)//2: conditions.append(row_data[row] > row_data[row+1]) else: conditions.append(row_data[row] < row_data[row+1]) result = reduce(lambda x, y: x & y, conditions) return result # Range midpoint acts as Resistance def is_resistance(row_data) -> bool: conditions = [] for row in range(len(row_data)-1): if row < len(row_data)//2: conditions.append(row_data[row] < row_data[row+1]) else: conditions.append(row_data[row] > row_data[row+1]) result = reduce(lambda x, y: x & y, conditions) return result # Elliot Wave Oscillator def ewo(dataframe, sma1_length=5, sma2_length=35): sma1 = ta.EMA(dataframe, timeperiod=sma1_length) sma2 = ta.EMA(dataframe, timeperiod=sma2_length) smadif = (sma1 - sma2) / dataframe['close'] * 100 return smadif # Chaikin Money Flow def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series: """Chaikin Money Flow (CMF) It measures the amount of Money Flow Volume over a specific period. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf Args: dataframe(pandas.Dataframe): dataframe containing ohlcv n(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ mfv = ((dataframe['close'] - dataframe['low']) - (dataframe['high'] - dataframe['close'])) / (dataframe['high'] - dataframe['low']) mfv = mfv.fillna(0.0) # float division by zero mfv *= dataframe['volume'] cmf = (mfv.rolling(n, min_periods=0).sum() / dataframe['volume'].rolling(n, min_periods=0).sum()) if fillna: cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0) return Series(cmf, name='cmf') # Williams %R def williams_r(dataframe: DataFrame, period: int = 14) -> Series: """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams. Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between, of its recent trading range. The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest). """ highest_high = dataframe["high"].rolling(center=False, window=period).max() lowest_low = dataframe["low"].rolling(center=False, window=period).min() WR = Series( (highest_high - dataframe["close"]) / (highest_high - lowest_low), name=f"{period} Williams %R", ) return WR * -100 # Volume Weighted Moving Average def vwma(dataframe: DataFrame, length: int = 10): """Indicator: Volume Weighted Moving Average (VWMA)""" # Calculate Result pv = dataframe['close'] * dataframe['volume'] vwma = Series(ta.SMA(pv, timeperiod=length) / ta.SMA(dataframe['volume'], timeperiod=length)) vwma = vwma.fillna(0, inplace=True) return vwma # Exponential moving average of a volume weighted simple moving average def ema_vwma_osc(dataframe, len_slow_ma): slow_ema = Series(ta.EMA(vwma(dataframe, len_slow_ma), len_slow_ma)) return ((slow_ema - slow_ema.shift(1)) / slow_ema.shift(1)) * 100 def t3_average(dataframe, length=5): """ T3 Average by HPotter on Tradingview https://www.tradingview.com/script/qzoC9H1I-T3-Average/ """ df = dataframe.copy() df['xe1'] = ta.EMA(df['close'], timeperiod=length) df['xe1'].fillna(0, inplace=True) df['xe2'] = ta.EMA(df['xe1'], timeperiod=length) df['xe2'].fillna(0, inplace=True) df['xe3'] = ta.EMA(df['xe2'], timeperiod=length) df['xe3'].fillna(0, inplace=True) df['xe4'] = ta.EMA(df['xe3'], timeperiod=length) df['xe4'].fillna(0, inplace=True) df['xe5'] = ta.EMA(df['xe4'], timeperiod=length) df['xe5'].fillna(0, inplace=True) df['xe6'] = ta.EMA(df['xe5'], timeperiod=length) df['xe6'].fillna(0, inplace=True) b = 0.7 c1 = -b * b * b c2 = 3 * b * b + 3 * b * b * b c3 = -6 * b * b - 3 * b - 3 * b * b * b c4 = 1 + 3 * b + b * b * b + 3 * b * b df['T3Average'] = c1 * df['xe6'] + c2 * df['xe5'] + c3 * df['xe4'] + c4 * df['xe3'] return df['T3Average'] # Pivot Points - 3 variants - daily recommended def pivot_points(dataframe: DataFrame, mode = 'fibonacci') -> Series: if mode == 'simple': hlc3_pivot = (dataframe['high'] + dataframe['low'] + dataframe['close']).shift(1) / 3 res1 = hlc3_pivot * 2 - dataframe['low'].shift(1) sup1 = hlc3_pivot * 2 - dataframe['high'].shift(1) res2 = hlc3_pivot + (dataframe['high'] - dataframe['low']).shift() sup2 = hlc3_pivot - (dataframe['high'] - dataframe['low']).shift() res3 = hlc3_pivot * 2 + (dataframe['high'] - 2 * dataframe['low']).shift() sup3 = hlc3_pivot * 2 - (2 * dataframe['high'] - dataframe['low']).shift() return hlc3_pivot, res1, res2, res3, sup1, sup2, sup3 elif mode == 'fibonacci': hlc3_pivot = (dataframe['high'] + dataframe['low'] + dataframe['close']).shift(1) / 3 hl_range = (dataframe['high'] - dataframe['low']).shift(1) res1 = hlc3_pivot + 0.382 * hl_range sup1 = hlc3_pivot - 0.382 * hl_range res2 = hlc3_pivot + 0.618 * hl_range sup2 = hlc3_pivot - 0.618 * hl_range res3 = hlc3_pivot + 1 * hl_range sup3 = hlc3_pivot - 1 * hl_range return hlc3_pivot, res1, res2, res3, sup1, sup2, sup3 elif mode == 'DeMark': demark_pivot_lt = (dataframe['low'] * 2 + dataframe['high'] + dataframe['close']) demark_pivot_eq = (dataframe['close'] * 2 + dataframe['low'] + dataframe['high']) demark_pivot_gt = (dataframe['high'] * 2 + dataframe['low'] + dataframe['close']) demark_pivot = np.where((dataframe['close'] < dataframe['open']), demark_pivot_lt, np.where((dataframe['close'] > dataframe['open']), demark_pivot_gt, demark_pivot_eq)) dm_pivot = demark_pivot / 4 dm_res = demark_pivot / 2 - dataframe['low'] dm_sup = demark_pivot / 2 - dataframe['high'] return dm_pivot, dm_res, dm_sup # Heikin Ashi candles def heikin_ashi(dataframe, smooth_inputs = False, smooth_outputs = False, length = 10): df = dataframe[['open','close','high','low']].copy().fillna(0) if smooth_inputs: df['open_s'] = ta.EMA(df['open'], timeframe = length) df['high_s'] = ta.EMA(df['high'], timeframe = length) df['low_s'] = ta.EMA(df['low'], timeframe = length) df['close_s'] = ta.EMA(df['close'],timeframe = length) open_ha = (df['open_s'].shift(1) + df['close_s'].shift(1)) / 2 high_ha = df.loc[:, ['high_s', 'open_s', 'close_s']].max(axis=1) low_ha = df.loc[:, ['low_s', 'open_s', 'close_s']].min(axis=1) close_ha = (df['open_s'] + df['high_s'] + df['low_s'] + df['close_s'])/4 else: open_ha = (df['open'].shift(1) + df['close'].shift(1)) / 2 high_ha = df.loc[:, ['high', 'open', 'close']].max(axis=1) low_ha = df.loc[:, ['low', 'open', 'close']].min(axis=1) close_ha = (df['open'] + df['high'] + df['low'] + df['close'])/4 open_ha = open_ha.fillna(0) high_ha = high_ha.fillna(0) low_ha = low_ha.fillna(0) close_ha = close_ha.fillna(0) if smooth_outputs: open_sha = ta.EMA(open_ha, timeframe = length) high_sha = ta.EMA(high_ha, timeframe = length) low_sha = ta.EMA(low_ha, timeframe = length) close_sha = ta.EMA(close_ha, timeframe = length) return open_sha, close_sha, low_sha else: return open_ha, close_ha, low_ha # Peak Percentage Change def range_percent_change(self, dataframe: DataFrame, method, length: int) -> float: """ Rolling Percentage Change Maximum across interval. :param dataframe: DataFrame The original OHLC dataframe :param method: High to Low / Open to Close :param length: int The length to look back """ if method == 'HL': return (dataframe['high'].rolling(length).max() - dataframe['low'].rolling(length).min()) / dataframe['low'].rolling(length).min() elif method == 'OC': return (dataframe['open'].rolling(length).max() - dataframe['close'].rolling(length).min()) / dataframe['close'].rolling(length).min() else: raise ValueError(f"Method {method} not defined!") # Percentage distance to top peak def top_percent_change(self, dataframe: DataFrame, length: int) -> float: """ Percentage change of the current close from the range maximum Open price :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ if length == 0: return (dataframe['open'] - dataframe['close']) / dataframe['close'] else: return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close'] # +---------------------------------------------------------------------------+ # | Classes | # +---------------------------------------------------------------------------+ class Cache: def __init__(self, path): self.path = path self.data = {} self._mtime = None self._previous_data = {} try: self.load() except FileNotFoundError: pass @staticmethod def rapidjson_load_kwargs(): return {"number_mode": rapidjson.NM_NATIVE} @staticmethod def rapidjson_dump_kwargs(): return {"number_mode": rapidjson.NM_NATIVE} def load(self): if not self._mtime or self.path.stat().st_mtime_ns != self._mtime: self._load() def save(self): if self.data != self._previous_data: self._save() def process_loaded_data(self, data): return data def _load(self): # This method only exists to simplify unit testing with self.path.open("r") as rfh: try: data = rapidjson.load( rfh, **self.rapidjson_load_kwargs() ) except rapidjson.JSONDecodeError as exc: log.error("Failed to load JSON from %s: %s", self.path, exc) else: self.data = self.process_loaded_data(data) self._previous_data = copy.deepcopy(self.data) self._mtime = self.path.stat().st_mtime_ns def _save(self): # This method only exists to simplify unit testing rapidjson.dump( self.data, self.path.open("w"), **self.rapidjson_dump_kwargs() ) self._mtime = self.path.stat().st_mtime self._previous_data = copy.deepcopy(self.data) class HoldsCache(Cache): @staticmethod def rapidjson_load_kwargs(): return { "number_mode": rapidjson.NM_NATIVE, "object_hook": HoldsCache._object_hook, } @staticmethod def rapidjson_dump_kwargs(): return { "number_mode": rapidjson.NM_NATIVE, "mapping_mode": rapidjson.MM_COERCE_KEYS_TO_STRINGS, } def save(self): raise RuntimeError("The holds cache does not allow programatical save") def process_loaded_data(self, data): trade_ids = data.get("trade_ids") trade_pairs = data.get("trade_pairs") if not trade_ids and not trade_pairs: return data open_trades = {} for trade in Trade.get_trades_proxy(is_open=True): open_trades[trade.id] = open_trades[trade.pair] = trade r_trade_ids = {} if trade_ids: if isinstance(trade_ids, dict): # New syntax for trade_id, profit_ratio in trade_ids.items(): if not isinstance(trade_id, int): log.error( "The trade_id(%s) defined under 'trade_ids' in %s is not an integer", trade_id, self.path ) continue if not isinstance(profit_ratio, float): log.error( "The 'profit_ratio' config value(%s) for trade_id %s in %s is not a float", profit_ratio, trade_id, self.path ) if trade_id in open_trades: formatted_profit_ratio = f"{profit_ratio * 100}%" log.warning( "The trade %s is configured to HOLD until the profit ratio of %s is met", open_trades[trade_id], formatted_profit_ratio ) r_trade_ids[trade_id] = profit_ratio else: log.warning( "The trade_id(%s) is no longer open. Please remove it from 'trade_ids' in %s", trade_id, self.path ) else: # Initial Syntax profit_ratio = data.get("profit_ratio") if profit_ratio: if not isinstance(profit_ratio, float): log.error( "The 'profit_ratio' config value(%s) in %s is not a float", profit_ratio, self.path ) else: profit_ratio = 0.005 formatted_profit_ratio = f"{profit_ratio * 100}%" for trade_id in trade_ids: if not isinstance(trade_id, int): log.error( "The trade_id(%s) defined under 'trade_ids' in %s is not an integer", trade_id, self.path ) continue if trade_id in open_trades: log.warning( "The trade %s is configured to HOLD until the profit ratio of %s is met", open_trades[trade_id], formatted_profit_ratio ) r_trade_ids[trade_id] = profit_ratio else: log.warning( "The trade_id(%s) is no longer open. Please remove it from 'trade_ids' in %s", trade_id, self.path ) r_trade_pairs = {} if trade_pairs: for trade_pair, profit_ratio in trade_pairs.items(): if not isinstance(trade_pair, str): log.error( "The trade_pair(%s) defined under 'trade_pairs' in %s is not a string", trade_pair, self.path ) continue if "/" not in trade_pair: log.error( "The trade_pair(%s) defined under 'trade_pairs' in %s does not look like " "a valid '/' formatted pair.", trade_pair, self.path ) continue if not isinstance(profit_ratio, float): log.error( "The 'profit_ratio' config value(%s) for trade_pair %s in %s is not a float", profit_ratio, trade_pair, self.path ) formatted_profit_ratio = f"{profit_ratio * 100}%" if trade_pair in open_trades: log.warning( "The trade %s is configured to HOLD until the profit ratio of %s is met", open_trades[trade_pair], formatted_profit_ratio ) else: log.warning( "The trade pair %s is configured to HOLD until the profit ratio of %s is met", trade_pair, formatted_profit_ratio ) r_trade_pairs[trade_pair] = profit_ratio r_data = {} if r_trade_ids: r_data["trade_ids"] = r_trade_ids if r_trade_pairs: r_data["trade_pairs"] = r_trade_pairs return r_data @staticmethod def _object_hook(data): _data = {} for key, value in data.items(): try: key = int(key) except ValueError: pass _data[key] = value return _data